Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    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 RandomForest extends Serializable with Logging

Annotations
@Since("1.2.0")
Source
RandomForest.scala
Linear Supertypes
Logging, Serializable, AnyRef, Any
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  1. RandomForest
  2. Logging
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  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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    Any
  5. def clone(): AnyRef
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    protected[lang]
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    AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  6. final def eq(arg0: AnyRef): Boolean
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    AnyRef
  7. def equals(arg0: AnyRef): Boolean
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    AnyRef → Any
  8. final def getClass(): Class[_ <: AnyRef]
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    Annotations
    @IntrinsicCandidate() @native()
  9. def hashCode(): Int
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    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  11. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
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    Logging
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. def isTraceEnabled(): Boolean
    Attributes
    protected
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    Logging
  14. def log: Logger
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    protected
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    Logging
  15. def logDebug(msg: => String, throwable: Throwable): Unit
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    protected
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    Logging
  16. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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    protected
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    Logging
  17. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
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    Logging
  18. def logDebug(msg: => String): Unit
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    protected
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    Logging
  19. def logError(msg: => String, throwable: Throwable): Unit
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    protected
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    Logging
  20. def logError(entry: LogEntry, throwable: Throwable): Unit
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    protected
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    Logging
  21. def logError(entry: LogEntry): Unit
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    protected
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    Logging
  22. def logError(msg: => String): Unit
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    protected
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    Logging
  23. def logInfo(msg: => String, throwable: Throwable): Unit
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    protected
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    Logging
  24. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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    protected
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    Logging
  25. def logInfo(entry: LogEntry): Unit
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    protected
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    Logging
  26. def logInfo(msg: => String): Unit
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    protected
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    Logging
  27. def logName: String
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    Logging
  28. def logTrace(msg: => String, throwable: Throwable): Unit
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    Logging
  29. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  30. def logTrace(entry: LogEntry): Unit
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    protected
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    Logging
  31. def logTrace(msg: => String): Unit
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    protected
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    Logging
  32. def logWarning(msg: => String, throwable: Throwable): Unit
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    Logging
  33. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  34. def logWarning(entry: LogEntry): Unit
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    Logging
  35. def logWarning(msg: => String): Unit
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    protected
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    Logging
  36. final def ne(arg0: AnyRef): Boolean
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    AnyRef
  37. final def notify(): Unit
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    AnyRef
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    @IntrinsicCandidate() @native()
  38. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  39. val supportedFeatureSubsetStrategies: Array[String]

    List of supported feature subset sampling strategies.

    List of supported feature subset sampling strategies.

    Annotations
    @Since("1.2.0")
  40. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int): RandomForestModel

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

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

    Annotations
    @Since("1.2.0")
  43. def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int = Utils.random.nextInt()): RandomForestModel

    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}.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "sqrt".

    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: 4)

    maxBins

    Maximum number of bins used for splitting features (suggested value: 100)

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

    Annotations
    @Since("1.2.0")
  44. def trainClassifier(input: RDD[LabeledPoint], strategy: Strategy, numTrees: Int, featureSubsetStrategy: String, seed: Int): RandomForestModel

    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}.

    strategy

    Parameters for training each tree in the forest.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "sqrt".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

    Annotations
    @Since("1.2.0")
  45. def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int): RandomForestModel

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

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

    Annotations
    @Since("1.2.0")
  46. def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int = Utils.random.nextInt()): RandomForestModel

    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}.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "onethird".

    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: 4)

    maxBins

    Maximum number of bins used for splitting features. (suggested value: 100)

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

    Annotations
    @Since("1.2.0")
  47. def trainRegressor(input: RDD[LabeledPoint], strategy: Strategy, numTrees: Int, featureSubsetStrategy: String, seed: Int): RandomForestModel

    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.

    strategy

    Parameters for training each tree in the forest.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "onethird".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

    Annotations
    @Since("1.2.0")
  48. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws(classOf[java.lang.InterruptedException])
  49. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()
  50. final def wait(): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException])
  51. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
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    protected[lang]
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

Inherited from Serializable

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