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The AWS Java SDK for Amazon Machine Learning module holds the client classes that is used for communicating with Amazon Machine Learning Service

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                                                                                                                                                                                                                    A string that is found at the beginning of a variable, such as Name or Id.

                                                                                                                                                                                                                    For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this Evaluation, select Name for the FilterVariable and any of the following strings for the Prefix:

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                                                                                                                                                                                                                                                                                                                                                        The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

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                                                                                                                                                                                                                                                                                                                                                        The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

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                                                                                                                                                                                                                                                                                                                                                        The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@param recipe The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

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                                                                                                                                                                                                                                                                                                                                                        The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@return The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@param recipe The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        ", "setterMethodName" : "setRecipe", "setterModel" : { "variableDeclarationType" : "String", "variableName" : "recipe", "variableType" : "String", "documentation" : null, "simpleType" : "String", "variableSetterType" : "String" }, "simple" : true, "timestampFormat" : null, "variable" : { "variableDeclarationType" : "String", "variableName" : "recipe", "variableType" : "String", "documentation" : "

                                                                                                                                                                                                                                                                                                                                                        The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@param schema The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@return Returns a reference to this object so that method calls can be chained together.", "fluentSetterMethodName" : "schema", "getterDocumentation" : "

                                                                                                                                                                                                                                                                                                                                                        The schema used by all of the data files referenced by the DataSource.

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                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@return The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        \n@param schema The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                        The schema used by all of the data files referenced by the DataSource.

                                                                                                                                                                                                                                                                                                                                                        Note

                                                                                                                                                                                                                                                                                                                                                        This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                        ", "simpleType" : "String", "variableSetterType" : "String" }, "xmlAttribute" : false, "xmlNameSpaceUri" : null } ], "membersAsMap" : { "ComputeTime" : { "beanStyleGetterMethodName" : "getComputeTime", "beanStyleSetterMethodName" : "setComputeTime", "c2jName" : "ComputeTime", "c2jShape" : "LongType", "defaultConsumerFluentSetterDocumentation" : "

                                                                                                                                                                                                                                                                                                                                                        The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

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                                                                                                                                                                                                                                                                                                                                                        A link to the file that contains logs of the CreateMLModel operation.

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                                                                                                                                                                                                                                                                                                                                                        The MLModel ID, which is same as the MLModelId in the request.

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                                                                                                                                                                                                                                                                                                                                                        The MLModel ID, which is same as the MLModelId in the request.

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                                                                                                                                                                                                                                                                                                                                                        The MLModel ID, which is same as the MLModelId in the request.

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                                                                                                                                                                                                                                                                                                                                                        The MLModel ID, which is same as the MLModelId in the request.

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                                                                                                                                                                                                                                                                                                                                                        The MLModel ID, which is same as the MLModelId in the request.

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                                                                                                                                                                                                                                                                                                                                                        Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                        Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                        Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                          Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                          \n

                                                                                                                                                                                                                                                                                                                                                          \nIf the service returns an enum value that is not available in the current SDK version, {@link #mlModelType} will return {@link MLModelType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from {@link #mlModelTypeAsString}.\n

                                                                                                                                                                                                                                                                                                                                                          \n@return Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                            Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                              Identifies the MLModel category. The following are the available types:

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A description of the most recent details about accessing the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              A user-supplied name or description of the MLModel.

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                                                                                                                                                                                                                                                                                                                                                              The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

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                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                              The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                              The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                              \n@param recipe The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

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                                                                                                                                                                                                                                                                                                                                                              The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                              \n@return The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                              ", "getterModel" : { "returnType" : "String", "documentation" : null }, "http" : { "additionalMarshallingPath" : null, "additionalUnmarshallingPath" : null, "flattened" : false, "greedy" : false, "header" : false, "isPayload" : false, "isStreaming" : false, "location" : null, "marshallLocation" : "PAYLOAD", "marshallLocationName" : "Recipe", "queryString" : false, "requiresLength" : false, "statusCode" : false, "unmarshallLocationName" : "Recipe", "uri" : false }, "idempotencyToken" : false, "isBinary" : false, "jsonValue" : false, "list" : false, "listModel" : null, "map" : false, "mapModel" : null, "marshallingType" : "STRING", "name" : "Recipe", "sensitive" : false, "setterDocumentation" : "

                                                                                                                                                                                                                                                                                                                                                              The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

                                                                                                                                                                                                                                                                                                                                                              Note

                                                                                                                                                                                                                                                                                                                                                              This parameter is provided as part of the verbose format.

                                                                                                                                                                                                                                                                                                                                                              \n@param recipe The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                    For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \nThis is a convenience that creates an instance of the {@link String.Builder} avoiding the need to create one manually via {@link String#builder()}.\n\nWhen the {@link Consumer} completes, {@link String.Builder#build()} is called immediately and its result is passed to {@link #dataRearrangement(String)}.\n@param dataRearrangement a consumer that will call methods on {@link String.Builder}\n@return Returns a reference to this object so that method calls can be chained together.\n@see #dataRearrangement(String)", "deprecated" : false, "documentation" : "

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    ", "endpointDiscoveryId" : false, "enumType" : null, "eventHeader" : false, "eventPayload" : false, "fluentEnumGetterMethodName" : null, "fluentEnumSetterMethodName" : null, "fluentGetterMethodName" : "dataRearrangement", "fluentSetterDocumentation" : "

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • \n@return Returns a reference to this object so that method calls can be chained together.", "fluentSetterMethodName" : "dataRearrangement", "getterDocumentation" : "

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n@return A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                        A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                          A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n@return A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • complement

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \nThis is a convenience that creates an instance of the {@link String.Builder} avoiding the need to create one manually via {@link String#builder()}.\n\nWhen the {@link Consumer} completes, {@link String.Builder#build()} is called immediately and its result is passed to {@link #dataRearrangement(String)}.\n@param dataRearrangement a consumer that will call methods on {@link String.Builder}\n@return Returns a reference to this object so that method calls can be chained together.\n@see #dataRearrangement(String)", "deprecated" : false, "documentation" : "

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n@return A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • complement

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n@return A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \n@return A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for evaluation: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                There are multiple parameters that control what data is used to create a datasource:

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Datasource for evaluation: {\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • strategy

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • percentEnd

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                • complement

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Datasource for training: {\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3://my_s3_path/bucket/file.csv\", \"complement\":\"true\"}}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        There are multiple parameters that control what data is used to create a datasource:

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentBegin

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • percentEnd

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        • strategy

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n@param dataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

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