com.amazonaws.services.sagemaker.model.AutoMLJobObjective Maven / Gradle / Ivy
/*
* Copyright 2015-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Specifies a metric to minimize or maximize as the objective of a job.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobObjective implements Serializable, Cloneable, StructuredPojo {
/**
*
* The name of the objective metric used to measure the predictive quality of a machine learning system. This metric
* is optimized during training to provide the best estimate for model parameter values from data.
*
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
* and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
* the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
* might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly and
* incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
* class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
* zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be true
* when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
* predictions to all positive predictions (including the false positives) in a data set and measures the quality of
* the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
* predictions to all actual positive instances and measures how completely a model predicts the actual class
* members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
* typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
* possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
* algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
* into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
* positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
* threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
* positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
* provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
* score can also be interpreted as the probability that a randomly selected positive data point is more likely to
* be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
* perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
* random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
* have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
* positive class in binary classification. Then used these values to calculate the F1 score for each class and
* average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
* performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
*
*/
private String metricName;
/**
*
* The name of the objective metric used to measure the predictive quality of a machine learning system. This metric
* is optimized during training to provide the best estimate for model parameter values from data.
*
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
* and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
* the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
* might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly and
* incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
* class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
* zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be true
* when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
* predictions to all positive predictions (including the false positives) in a data set and measures the quality of
* the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
* predictions to all actual positive instances and measures how completely a model predicts the actual class
* members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
* typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
* possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
* algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
* into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
* positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
* threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
* positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
* provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
* score can also be interpreted as the probability that a randomly selected positive data point is more likely to
* be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
* perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
* random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
* have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
* positive class in binary classification. Then used these values to calculate the F1 score for each class and
* average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
* performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
*
*
* @param metricName
* The name of the objective metric used to measure the predictive quality of a machine learning system. This
* metric is optimized during training to provide the best estimate for model parameter values from data.
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the
* predicted and actual values. It is used for regression. MSE values are always positive, the better a model
* is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
* to dominate the MSE which might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly
* and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
* predicted class values are to the actual values. Accuracy values vary between zero and one, one being
* perfect accuracy and zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be
* true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
* true positive predictions to all positive predictions (including the false positives) in a data set and
* measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
* ratio of the true positive predictions to all actual positive instances and measures how completely a
* model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
* equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
* between zero and one, one being the best possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
* classification by algorithms such as logistic regression that return probabilities. A threshold is needed
* to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
* curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
* (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
* the threshold results in fewer false positives but more false negatives. AUC is the area under this
* receiver operating characteristic curve and so provides an aggregated measure of the model performance
* across all possible classification thresholds. The AUC score can also be interpreted as the probability
* that a randomly selected positive data point is more likely to be predicted positive than a randomly
* selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
* not better than a random classifier. Values less that one half predict worse than a random predictor and
* such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
* you have multiple classes to predict. You just calculate the precision and recall for each class as you
* did for the positive class in binary classification. Then used these values to calculate the F1 score for
* each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
* being the best possible performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
* @see AutoMLMetricEnum
*/
public void setMetricName(String metricName) {
this.metricName = metricName;
}
/**
*
* The name of the objective metric used to measure the predictive quality of a machine learning system. This metric
* is optimized during training to provide the best estimate for model parameter values from data.
*
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
* and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
* the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
* might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly and
* incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
* class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
* zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be true
* when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
* predictions to all positive predictions (including the false positives) in a data set and measures the quality of
* the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
* predictions to all actual positive instances and measures how completely a model predicts the actual class
* members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
* typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
* possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
* algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
* into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
* positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
* threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
* positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
* provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
* score can also be interpreted as the probability that a randomly selected positive data point is more likely to
* be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
* perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
* random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
* have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
* positive class in binary classification. Then used these values to calculate the F1 score for each class and
* average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
* performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
*
*
* @return The name of the objective metric used to measure the predictive quality of a machine learning system.
* This metric is optimized during training to provide the best estimate for model parameter values from
* data.
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the
* predicted and actual values. It is used for regression. MSE values are always positive, the better a
* model is at predicting the actual values the smaller the MSE value. When the data contains outliers, they
* tend to dominate the MSE which might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly
* and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
* predicted class values are to the actual values. Accuracy values vary between zero and one, one being
* perfect accuracy and zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to
* be true when they match their actual (correct) class; false when they do not. Precision is the ratio of
* the true positive predictions to all positive predictions (including the false positives) in a data set
* and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity)
* is the ratio of the true positive predictions to all actual positive instances and measures how
* completely a model predicts the actual class members in a data set. The standard F1 score weighs
* precision and recall equally. But which metric is paramount typically depends on specific aspects of a
* problem. F1 scores vary between zero and one, one being the best possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
* classification by algorithms such as logistic regression that return probabilities. A threshold is needed
* to map the probabilities into classifications. The relevant curve is the receiver operating
* characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false
* positive rate (FPR) as a function of the threshold value, above which a prediction is considered
* positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the
* area under this receiver operating characteristic curve and so provides an aggregated measure of the
* model performance across all possible classification thresholds. The AUC score can also be interpreted as
* the probability that a randomly selected positive data point is more likely to be predicted positive than
* a randomly selected negative example. AUC scores vary between zero and one, one being perfect accuracy
* and one half not better than a random classifier. Values less that one half predict worse than a random
* predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
* you have multiple classes to predict. You just calculate the precision and recall for each class as you
* did for the positive class in binary classification. Then used these values to calculate the F1 score for
* each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
* being the best possible performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
* @see AutoMLMetricEnum
*/
public String getMetricName() {
return this.metricName;
}
/**
*
* The name of the objective metric used to measure the predictive quality of a machine learning system. This metric
* is optimized during training to provide the best estimate for model parameter values from data.
*
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
* and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
* the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
* might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly and
* incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
* class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
* zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be true
* when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
* predictions to all positive predictions (including the false positives) in a data set and measures the quality of
* the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
* predictions to all actual positive instances and measures how completely a model predicts the actual class
* members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
* typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
* possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
* algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
* into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
* positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
* threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
* positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
* provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
* score can also be interpreted as the probability that a randomly selected positive data point is more likely to
* be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
* perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
* random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
* have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
* positive class in binary classification. Then used these values to calculate the F1 score for each class and
* average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
* performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
*
*
* @param metricName
* The name of the objective metric used to measure the predictive quality of a machine learning system. This
* metric is optimized during training to provide the best estimate for model parameter values from data.
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the
* predicted and actual values. It is used for regression. MSE values are always positive, the better a model
* is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
* to dominate the MSE which might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly
* and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
* predicted class values are to the actual values. Accuracy values vary between zero and one, one being
* perfect accuracy and zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be
* true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
* true positive predictions to all positive predictions (including the false positives) in a data set and
* measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
* ratio of the true positive predictions to all actual positive instances and measures how completely a
* model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
* equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
* between zero and one, one being the best possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
* classification by algorithms such as logistic regression that return probabilities. A threshold is needed
* to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
* curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
* (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
* the threshold results in fewer false positives but more false negatives. AUC is the area under this
* receiver operating characteristic curve and so provides an aggregated measure of the model performance
* across all possible classification thresholds. The AUC score can also be interpreted as the probability
* that a randomly selected positive data point is more likely to be predicted positive than a randomly
* selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
* not better than a random classifier. Values less that one half predict worse than a random predictor and
* such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
* you have multiple classes to predict. You just calculate the precision and recall for each class as you
* did for the positive class in binary classification. Then used these values to calculate the F1 score for
* each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
* being the best possible performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMetricEnum
*/
public AutoMLJobObjective withMetricName(String metricName) {
setMetricName(metricName);
return this;
}
/**
*
* The name of the objective metric used to measure the predictive quality of a machine learning system. This metric
* is optimized during training to provide the best estimate for model parameter values from data.
*
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
* and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
* the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
* might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly and
* incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
* class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
* zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be true
* when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
* predictions to all positive predictions (including the false positives) in a data set and measures the quality of
* the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
* predictions to all actual positive instances and measures how completely a model predicts the actual class
* members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
* typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
* possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
* algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
* into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
* positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
* threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
* positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
* provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
* score can also be interpreted as the probability that a randomly selected positive data point is more likely to
* be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
* perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
* random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
* have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
* positive class in binary classification. Then used these values to calculate the F1 score for each class and
* average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
* performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
*
*
* @param metricName
* The name of the objective metric used to measure the predictive quality of a machine learning system. This
* metric is optimized during training to provide the best estimate for model parameter values from data.
*
* Here are the options:
*
*
* -
*
* MSE: The mean squared error (MSE) is the average of the squared differences between the
* predicted and actual values. It is used for regression. MSE values are always positive, the better a model
* is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
* to dominate the MSE which might cause subpar prediction performance.
*
*
* -
*
* Accuracy: The ratio of the number correctly classified items to the total number (correctly
* and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
* predicted class values are to the actual values. Accuracy values vary between zero and one, one being
* perfect accuracy and zero perfect inaccuracy.
*
*
* -
*
* F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
* classification into classes traditionally referred to as positive and negative. Predictions are said to be
* true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
* true positive predictions to all positive predictions (including the false positives) in a data set and
* measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
* ratio of the true positive predictions to all actual positive instances and measures how completely a
* model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
* equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
* between zero and one, one being the best possible performance and zero the worst.
*
*
* -
*
* AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
* classification by algorithms such as logistic regression that return probabilities. A threshold is needed
* to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
* curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
* (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
* the threshold results in fewer false positives but more false negatives. AUC is the area under this
* receiver operating characteristic curve and so provides an aggregated measure of the model performance
* across all possible classification thresholds. The AUC score can also be interpreted as the probability
* that a randomly selected positive data point is more likely to be predicted positive than a randomly
* selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
* not better than a random classifier. Values less that one half predict worse than a random predictor and
* such consistently bad predictors can be inverted to obtain better than random predictors.
*
*
* -
*
* F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
* you have multiple classes to predict. You just calculate the precision and recall for each class as you
* did for the positive class in binary classification. Then used these values to calculate the F1 score for
* each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
* being the best possible performance and zero the worst.
*
*
*
*
* If you do not specify a metric explicitly, the default behavior is to automatically use:
*
*
* -
*
* MSE: for regression.
*
*
* -
*
* F1: for binary classification
*
*
* -
*
* Accuracy: for multiclass classification.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMetricEnum
*/
public AutoMLJobObjective withMetricName(AutoMLMetricEnum metricName) {
this.metricName = metricName.toString();
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getMetricName() != null)
sb.append("MetricName: ").append(getMetricName());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AutoMLJobObjective == false)
return false;
AutoMLJobObjective other = (AutoMLJobObjective) obj;
if (other.getMetricName() == null ^ this.getMetricName() == null)
return false;
if (other.getMetricName() != null && other.getMetricName().equals(this.getMetricName()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getMetricName() == null) ? 0 : getMetricName().hashCode());
return hashCode;
}
@Override
public AutoMLJobObjective clone() {
try {
return (AutoMLJobObjective) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.AutoMLJobObjectiveMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}