BoostingStrategy

:: 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]

Annotations: @Experimental()

Linear Supertypes

Product, Equals, Serializable, Serializable, AnyRef, Any

Instance Constructors

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]

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
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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

Definition Classes
AnyRef
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def getLearningRate(): Double
def getLoss(): Loss
def getNumIterations(): Int
def getTreeStrategy(): Strategy
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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]
var loss: Loss

Loss function used for minimization during gradient boosting.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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.
def setLearningRate(arg0: Double): Unit
def setLoss(arg0: Loss): Unit
def setNumIterations(arg0: Int): Unit
def setTreeStrategy(arg0: Strategy): Unit
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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.
final def wait(): Unit

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

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

Definition Classes
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Annotations
@throws( ... )

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

Instance Constructors

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getLearningRate(): Double

def getLoss(): Loss

def getNumIterations(): Int

def getTreeStrategy(): Strategy

final def isInstanceOf[T0]: Boolean

var learningRate: Double

var loss: Loss

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var numIterations: Int

def setLearningRate(arg0: Double): Unit

def setLoss(arg0: Loss): Unit

def setNumIterations(arg0: Int): Unit

def setTreeStrategy(arg0: Strategy): Unit

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

var treeStrategy: Strategy

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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Inherited from Equals

Inherited from Serializable

Inherited from Serializable

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