Package org.apache.spark.mllib.tree

Class DecisionTree

Object
org.apache.spark.mllib.tree.DecisionTree
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging
public class DecisionTree extends Object implements Serializable, org.apache.spark.internal.Logging
A class which implements a decision tree learning algorithm for classification and regression. It supports both continuous and categorical features.

param: strategy The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc. param: seed Random seed.

See Also:

  • Constructor Details

    • DecisionTree

      public DecisionTree(Strategy strategy)
      Parameters:
      strategy - The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.

  • Method Details

    • train

      public static DecisionTreeModel train(RDD<LabeledPoint> input, Strategy strategy)
      Method to train a decision tree model. The method supports binary and multiclass classification and regression.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
      strategy - The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.
      Returns:
      DecisionTreeModel that can be used for prediction.

      Note:
      Using org.apache.spark.mllib.tree.DecisionTree.trainClassifier and org.apache.spark.mllib.tree.DecisionTree.trainRegressor is recommended to clearly separate classification and regression.

    • train

      public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth)
      Method to train a decision tree model. The method supports binary and multiclass classification and regression.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
      algo - Type of decision tree, either classification or regression.
      impurity - Criterion used for information gain calculation.
      maxDepth - Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
      Returns:
      DecisionTreeModel that can be used for prediction.

      Note:
      Using org.apache.spark.mllib.tree.DecisionTree.trainClassifier and org.apache.spark.mllib.tree.DecisionTree.trainRegressor is recommended to clearly separate classification and regression.

    • train

      public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses)
      Method to train a decision tree model. The method supports binary and multiclass classification and regression.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
      algo - Type of decision tree, either classification or regression.
      impurity - Criterion used for information gain calculation.
      maxDepth - Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
      numClasses - Number of classes for classification. Default value of 2.
      Returns:
      DecisionTreeModel that can be used for prediction.

      Note:
      Using org.apache.spark.mllib.tree.DecisionTree.trainClassifier and org.apache.spark.mllib.tree.DecisionTree.trainRegressor is recommended to clearly separate classification and regression.

    • train

      public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo)
      Method to train a decision tree model. The method supports binary and multiclass classification and regression.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
      algo - Type of decision tree, either classification or regression.
      impurity - Criterion used for information gain calculation.
      maxDepth - Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
      numClasses - Number of classes for classification. Default value of 2.
      maxBins - Maximum number of bins used for splitting features.
      quantileCalculationStrategy - Algorithm for calculating quantiles.
      categoricalFeaturesInfo - Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
      Returns:
      DecisionTreeModel that can be used for prediction.

      Note:
      Using org.apache.spark.mllib.tree.DecisionTree.trainClassifier and org.apache.spark.mllib.tree.DecisionTree.trainRegressor is recommended to clearly separate classification and regression.

    • trainClassifier

      public static DecisionTreeModel trainClassifier(RDD<LabeledPoint> input, int numClasses, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
      Method to train a decision tree model for binary or multiclass classification.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.
      numClasses - Number of classes for classification.
      categoricalFeaturesInfo - Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
      impurity - Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy".
      maxDepth - Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)
      maxBins - Maximum number of bins used for splitting features. (suggested value: 32)
      Returns:
      DecisionTreeModel that can be used for prediction.

    • trainClassifier

      public static DecisionTreeModel trainClassifier(JavaRDD<LabeledPoint> input, int numClasses, Map<Integer,Integer> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
      Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainClassifier
      Parameters:
      input - (undocumented)
      numClasses - (undocumented)
      categoricalFeaturesInfo - (undocumented)
      impurity - (undocumented)
      maxDepth - (undocumented)
      maxBins - (undocumented)
      Returns:
      (undocumented)

    • trainRegressor

      public static DecisionTreeModel trainRegressor(RDD<LabeledPoint> input, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
      Method to train a decision tree model for regression.

      Parameters:
      input - Training dataset: RDD of LabeledPoint. Labels are real numbers.
      categoricalFeaturesInfo - Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
      impurity - Criterion used for information gain calculation. The only supported value for regression is "variance".
      maxDepth - Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)
      maxBins - Maximum number of bins used for splitting features. (suggested value: 32)
      Returns:
      DecisionTreeModel that can be used for prediction.

    • trainRegressor

      public static DecisionTreeModel trainRegressor(JavaRDD<LabeledPoint> input, Map<Integer,Integer> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
      Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainRegressor
      Parameters:
      input - (undocumented)
      categoricalFeaturesInfo - (undocumented)
      impurity - (undocumented)
      maxDepth - (undocumented)
      maxBins - (undocumented)
      Returns:
      (undocumented)

    • org$apache$spark$internal$Logging$$log_

      public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

    • org$apache$spark$internal$Logging$$log__$eq

      public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

    • LogStringContext

      public static org.apache.spark.internal.Logging.LogStringContext LogStringContext(scala.StringContext sc)

    • run

      public DecisionTreeModel run(RDD<LabeledPoint> input)
      Method to train a decision tree model over an RDD

      Parameters:
      input - Training data: RDD of LabeledPoint.
      Returns:
      DecisionTreeModel that can be used for prediction.