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
  • Algo
  • BoostingStrategy
  • FeatureType
  • QuantileStrategy
  • Strategy

case class BoostingStrategy(treeStrategy: Strategy, loss: Loss, numIterations: Int = 100, learningRate: Double = 0.1, validationTol: Double = 0.001) extends Serializable with Product

Configuration options for org.apache.spark.mllib.tree.GradientBoostedTrees.

treeStrategy

Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored.

loss

Loss function used for minimization during gradient boosting.

numIterations

Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model.

learningRate

Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]

validationTol

validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

Annotations
@Since( "1.2.0" )
Source
BoostingStrategy.scala
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Instance Constructors

  1. new BoostingStrategy(treeStrategy: Strategy, loss: Loss, numIterations: Int = 100, learningRate: Double = 0.1, validationTol: Double = 0.001)

    treeStrategy

    Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored.

    loss

    Loss function used for minimization during gradient boosting.

    numIterations

    Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model.

    learningRate

    Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]

    validationTol

    validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

    Annotations
    @Since( "1.4.0" )

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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    @throws( ... ) @native()
  6. final def eq(arg0: AnyRef): Boolean
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  7. def finalize(): Unit
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  8. final def getClass(): Class[_]
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  9. def getLearningRate(): Double
    Annotations
    @Since( "1.2.0" )
  10. def getLoss(): Loss
    Annotations
    @Since( "1.2.0" )
  11. def getNumIterations(): Int
    Annotations
    @Since( "1.2.0" )
  12. def getTreeStrategy(): Strategy
    Annotations
    @Since( "1.2.0" )
  13. def getValidationTol(): Double
    Annotations
    @Since( "1.4.0" )
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. var learningRate: Double
    Annotations
    @Since( "1.2.0" )
  16. var loss: Loss
    Annotations
    @Since( "1.2.0" )
  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  18. final def notify(): Unit
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    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  20. var numIterations: Int
    Annotations
    @Since( "1.2.0" )
  21. def setLearningRate(arg0: Double): Unit
    Annotations
    @Since( "1.2.0" )
  22. def setLoss(arg0: Loss): Unit
    Annotations
    @Since( "1.2.0" )
  23. def setNumIterations(arg0: Int): Unit
    Annotations
    @Since( "1.2.0" )
  24. def setTreeStrategy(arg0: Strategy): Unit
    Annotations
    @Since( "1.2.0" )
  25. def setValidationTol(arg0: Double): Unit
    Annotations
    @Since( "1.4.0" )
  26. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  27. var treeStrategy: Strategy
    Annotations
    @Since( "1.2.0" )
  28. var validationTol: Double
    Annotations
    @Since( "1.4.0" )
  29. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit
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  31. final def wait(arg0: Long): Unit
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