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   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
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   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
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   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
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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.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.
   *
   * <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;
  