GradientBoostedTrees

:: 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:

This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
Both algorithms learn tree ensembles by minimizing loss functions.
TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
- When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.

Annotations: @Experimental()

Linear Supertypes

Logging, Serializable, Serializable, AnyRef, Any

Instance Constructors

new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

boostingStrategy
Parameters for the gradient boosting algorithm.

Value Members

final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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protected
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def log: Logger

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

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Logging
def logDebug(msg: ⇒ String): Unit

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

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protected
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def logError(msg: ⇒ String): Unit

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

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protected
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Logging
def logInfo(msg: ⇒ String): Unit

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protected
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def logName: String

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protected
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Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logTrace(msg: ⇒ String): Unit

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protected
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Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logWarning(msg: ⇒ String): Unit

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protected
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Logging
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees!#run.
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
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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@throws( ... )
final def wait(arg0: Long): Unit

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@throws( ... )

class GradientBoostedTrees extends Serializable with Logging

Instance Constructors

new GradientBoostedTrees(boostingStrategy: BoostingStrategy)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

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

def logDebug(msg: ⇒ String): Unit

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

def logError(msg: ⇒ String): Unit

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

def logInfo(msg: ⇒ String): Unit

def logName: String

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

def logTrace(msg: ⇒ String): Unit

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

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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