org.apache.spark.ml.attribute.package-info Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
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// The content here should be in sync with `package.scala`.
/**
* ML attributes
*
* The ML pipeline API uses {@link org.apache.spark.sql.Dataset}s as ML datasets.
* Each dataset consists of typed columns, e.g., string, double, vector, etc.
* However, knowing only the column type may not be sufficient to handle the data properly.
* For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices,
* which cannot be treated as numeric values in ML algorithms, and, for another instance, we may
* want to know the names and types of features stored in a vector column.
* ML attributes are used to provide additional information to describe columns in a dataset.
*
* ML columns
*
* A column with ML attributes attached is called an ML column.
* The data in ML columns are stored as double values, i.e., an ML column is either a scalar column
* of double values or a vector column.
* Columns of other types must be encoded into ML columns using transformers.
* We use {@link org.apache.spark.ml.attribute.Attribute} to describe a scalar ML column, and
* {@link org.apache.spark.ml.attribute.AttributeGroup} to describe a vector ML column.
* ML attributes are stored in the metadata field of the column schema.
*/
package org.apache.spark.ml.attribute;