Class/Object

org.apache.spark.mllib.tree

GradientBoostedTrees

Related Docs: object GradientBoostedTrees | package tree

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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:

Annotations
@Since( "1.2.0" )
Source
GradientBoostedTrees.scala
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Logging, Serializable, Serializable, AnyRef, Any
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Instance Constructors

  1. new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

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    boostingStrategy

    Parameters for the gradient boosting algorithm.

    Annotations
    @Since( "1.2.0" )

Value Members

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

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  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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  5. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  10. def hashCode(): Int

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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
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    Logging

  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  13. final def isInstanceOf[T0]: Boolean

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  14. def isTraceEnabled(): Boolean

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    protected
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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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  24. def logTrace(msg: ⇒ String): Unit

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  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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

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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

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    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" )

  31. def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel

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    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" )

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

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    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" )

  33. def runWithValidation(input: RDD[LabeledPoint], validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel

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    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" )

  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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    @throws( ... )

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

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

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