org.apache.spark.mllib.tree.configuration

BoostingStrategy

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

:: Experimental :: 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]

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@Experimental()
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Instance Constructors

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

    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]

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

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

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  3. final def ##(): Int

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  10. final def getClass(): Class[_]

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  11. def getLearningRate(): Double

  12. def getLoss(): Loss

  13. def getNumIterations(): Int

  14. def getTreeStrategy(): Strategy

  15. final def isInstanceOf[T0]: Boolean

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  16. var learningRate: Double

    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]

  17. var loss: Loss

    Loss function used for minimization during gradient boosting.

  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. var numIterations: Int

    Number of iterations of boosting.

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

  22. def setLearningRate(arg0: Double): Unit

  23. def setLoss(arg0: Loss): Unit

  24. def setNumIterations(arg0: Int): Unit

  25. def setTreeStrategy(arg0: Strategy): Unit

  26. final def synchronized[T0](arg0: ⇒ T0): T0

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  27. var treeStrategy: Strategy

    Parameters for the tree algorithm.

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

  28. final def wait(): Unit

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

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

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