Class/Object

org.apache.spark.mllib.tree.configuration

BoostingStrategy

Related Docs: object BoostingStrategy | package configuration

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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 > 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 <= 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.

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@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)

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    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 > 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 <= 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.

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    @Since( "1.4.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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  9. def getLearningRate(): Double

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    @Since( "1.2.0" )
  10. def getLoss(): Loss

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    @Since( "1.2.0" )
  11. def getNumIterations(): Int

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  12. def getTreeStrategy(): Strategy

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    @Since( "1.2.0" )
  13. def getValidationTol(): Double

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    @Since( "1.4.0" )
  14. final def isInstanceOf[T0]: Boolean

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  15. var learningRate: Double

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    Learning rate for shrinking the contribution of each estimator.

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

    Annotations
    @Since( "1.2.0" )
  16. var loss: Loss

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    Loss function used for minimization during gradient boosting.

    Loss function used for minimization during gradient boosting.

    Annotations
    @Since( "1.2.0" )
  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. var numIterations: Int

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    Number of iterations of boosting.

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

    Annotations
    @Since( "1.2.0" )
  21. def setLearningRate(arg0: Double): Unit

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    @Since( "1.2.0" )
  22. def setLoss(arg0: Loss): Unit

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    @Since( "1.2.0" )
  23. def setNumIterations(arg0: Int): Unit

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    @Since( "1.2.0" )
  24. def setTreeStrategy(arg0: Strategy): Unit

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    @Since( "1.2.0" )
  25. def setValidationTol(arg0: Double): Unit

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    @Since( "1.4.0" )
  26. final def synchronized[T0](arg0: ⇒ T0): T0

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  27. var treeStrategy: Strategy

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    Parameters for the tree algorithm.

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

    Annotations
    @Since( "1.2.0" )
  28. var validationTol: Double

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    validationTol is a condition which decides iteration termination when runWithValidation is used.

    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 > 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 <= 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" )
  29. final def wait(): Unit

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  30. final def wait(arg0: Long, arg1: Int): Unit

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  31. final def wait(arg0: Long): Unit

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