org.apache.spark.mllib.tree

GradientBoostedTrees

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
Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. GradientBoostedTrees
  2. Logging
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

    boostingStrategy

    Parameters for the gradient boosting algorithm.

    Annotations
    @Since( "1.2.0" )

Value Members

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

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

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  14. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  15. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  19. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  21. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  22. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  24. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  26. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  27. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  28. final def notify(): Unit

    Definition Classes
    AnyRef
  29. final def notifyAll(): Unit

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

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

    Annotations
    @Since( "1.2.0" )
  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

    Annotations
    @Since( "1.2.0" )
  32. def runWithValidation(input: JavaRDD[LabeledPoint], validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel

    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

    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

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

    Definition Classes
    AnyRef
  35. def toString(): String

    Definition Classes
    AnyRef → Any
  36. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped