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  • package root
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    root
  • package org
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    root
  • package apache
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    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package configuration
    Definition Classes
    tree
  • package impurity
    Definition Classes
    tree
  • package loss
    Definition Classes
    tree
  • package model
    Definition Classes
    tree
  • DecisionTree
  • GradientBoostedTrees
  • RandomForest

object DecisionTree extends Serializable with Logging

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@Since( "1.0.0" )
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Logging, Serializable, Serializable, AnyRef, Any
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  1. DecisionTree
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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    protected[java.lang]
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
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    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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    protected
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    Logging
  13. final def isInstanceOf[T0]: Boolean
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    Any
  14. def isTraceEnabled(): Boolean
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    protected
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    Logging
  15. def log: Logger
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    protected
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    Logging
  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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    protected
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    Logging
  17. def logDebug(msg: ⇒ String): Unit
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    protected
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    Logging
  18. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
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    Logging
  19. def logError(msg: ⇒ String): Unit
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    protected
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    Logging
  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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    protected
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    Logging
  21. def logInfo(msg: ⇒ String): Unit
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    protected
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    Logging
  22. def logName: String
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    protected
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    Logging
  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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    protected
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    Logging
  24. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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    protected
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    Logging
  26. def logWarning(msg: ⇒ String): Unit
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    protected
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    Logging
  27. final def ne(arg0: AnyRef): Boolean
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  28. final def notify(): Unit
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  29. final def notifyAll(): Unit
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  30. final def synchronized[T0](arg0: ⇒ T0): T0
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  31. def toString(): String
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  32. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int, maxBins: Int, quantileCalculationStrategy: QuantileStrategy, categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.0.0" )
    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.

  33. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int): DecisionTreeModel

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.2.0" )
    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.

  34. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int): DecisionTreeModel

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.0.0" )
    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.

  35. def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.0.0" )
    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.

  36. def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainClassifier

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainClassifier

    Annotations
    @Since( "1.1.0" )
  37. def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    Method to train a decision tree model for binary or multiclass classification.

    Method to train a decision tree model for binary or multiclass classification.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.1.0" )
  38. def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainRegressor

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainRegressor

    Annotations
    @Since( "1.1.0" )
  39. def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    Method to train a decision tree model for regression.

    Method to train a decision tree model for regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.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.

    Annotations
    @Since( "1.1.0" )
  40. final def wait(): Unit
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  41. final def wait(arg0: Long, arg1: Int): Unit
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  42. final def wait(arg0: Long): Unit
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