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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
* (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
* distributed under the License 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 org.apache.spark.mllib.tree.model
import org.apache.spark.mllib.tree.configuration.FeatureType._
/**
* Used for "binning" the feature values for faster best split calculation.
*
* For a continuous feature, the bin is determined by a low and a high split,
* where an example with featureValue falls into the bin s.t.
* lowSplit.threshold < featureValue <= highSplit.threshold.
*
* For ordered categorical features, there is a 1-1-1 correspondence between
* bins, splits, and feature values. The bin is determined by category/feature value.
* However, the bins are not necessarily ordered by feature value;
* they are ordered using impurity.
*
* For unordered categorical features, there is a 1-1 correspondence between bins, splits,
* where bins and splits correspond to subsets of feature values (in highSplit.categories).
* An unordered feature with k categories uses (1 << k - 1) - 1 bins, corresponding to all
* partitionings of categories into 2 disjoint, non-empty sets.
*
* @param lowSplit signifying the lower threshold for the continuous feature to be
* accepted in the bin
* @param highSplit signifying the upper threshold for the continuous feature to be
* accepted in the bin
* @param featureType type of feature -- categorical or continuous
* @param category categorical label value accepted in the bin for ordered features
*/
private[tree]
case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double)
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