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

class GradientBoostedTrees extends Serializable with Logging

A class that implements Stochastic Gradient Boosting for regression and binary classification.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost:

  • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
  • Both algorithms learn tree ensembles by minimizing loss functions.
  • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
    • When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.
Annotations
@Since("1.2.0")
Source
GradientBoostedTrees.scala
Linear Supertypes
Logging, Serializable, AnyRef, Any
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  1. GradientBoostedTrees
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Instance Constructors

  1. new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

    boostingStrategy

    Parameters for the gradient boosting algorithm.

    Annotations
    @Since("1.2.0")

Type Members

  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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    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
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    AnyRef → Any
  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
    Definition Classes
    AnyRef → Any
  8. final def getClass(): Class[_ <: AnyRef]
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    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  9. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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    protected
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    Logging
  11. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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    protected
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    Logging
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. def isTraceEnabled(): Boolean
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    protected
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    Logging
  14. def log: Logger
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    Logging
  15. def logDebug(msg: => String, throwable: Throwable): Unit
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    Logging
  16. def logDebug(entry: LogEntry, throwable: Throwable): Unit
    Attributes
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    Logging
  17. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: => String): Unit
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    protected
    Definition Classes
    Logging
  19. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
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    Logging
  20. def logError(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  21. def logError(entry: LogEntry): Unit
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    Logging
  22. def logError(msg: => String): Unit
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    Logging
  23. def logInfo(msg: => String, throwable: Throwable): Unit
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    Logging
  24. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  25. def logInfo(entry: LogEntry): Unit
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    Logging
  26. def logInfo(msg: => String): Unit
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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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    Logging
  31. def logTrace(msg: => String): Unit
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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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    Logging
  36. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  37. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  38. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  39. def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel

    Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.run.

    Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.run.

    Annotations
    @Since("1.2.0")
  40. def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel

    Method to train a gradient boosting model

    Method to train a gradient boosting model

    input

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

    returns

    GradientBoostedTreesModel that can be used for prediction.

    Annotations
    @Since("1.2.0")
  41. def runWithValidation(input: JavaRDD[LabeledPoint], validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel

    Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.runWithValidation.

    Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.runWithValidation.

    Annotations
    @Since("1.4.0")
  42. def runWithValidation(input: RDD[LabeledPoint], validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel

    Method to validate a gradient boosting model

    Method to validate a gradient boosting model

    input

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

    validationInput

    Validation dataset. This dataset should be different from the training dataset, but it should follow the same distribution. E.g., these two datasets could be created from an original dataset by using org.apache.spark.rdd.RDD.randomSplit()

    returns

    GradientBoostedTreesModel that can be used for prediction.

    Annotations
    @Since("1.4.0")
  43. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  44. def toString(): String
    Definition Classes
    AnyRef → Any
  45. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  46. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()
  47. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  48. 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]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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