spark-gpu-master/mllib/src/main/scala/org/apache/spark/ml/attribute/package-info.java
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 * (the "License"); you may not use this file except in compliance with
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 *    http://www.apache.org/licenses/LICENSE-2.0
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// The content here should be in sync with `package.scala`.

/**
 * <h2>ML attributes</h2>
 *
 * The ML pipeline API uses {@link org.apache.spark.sql.DataFrame}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.
 *
 * <h3>ML columns</h3>
 *
 * 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;
