org.apache.spark.mllib.tree

GradientBoostedTrees

class GradientBoostedTrees extends Serializable with Logging

:: Experimental :: 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:

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@Experimental()
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Instance Constructors

  1. new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

    boostingStrategy

    Parameters for the gradient boosting algorithm.

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logInfo(msg: ⇒ String): Unit

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  22. def logName: String

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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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  30. def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel

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

  31. 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

    a gradient boosted trees model that can be used for prediction

  32. def runWithValidation(input: JavaRDD[LabeledPoint], validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel

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

  33. 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

    a gradient boosted trees model that can be used for prediction

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

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  35. def toString(): String

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  36. final def wait(): Unit

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