BoostingStrategy

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]
validationTol: validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

Annotations: @Since( "1.2.0" )
Source: BoostingStrategy.scala

Linear Supertypes

Product, Equals, Serializable, Serializable, AnyRef, Any

Instance Constructors

new BoostingStrategy(treeStrategy: Strategy, loss: Loss, numIterations: Int = 100, learningRate: Double = 0.1, validationTol: Double = 0.001)

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]
validationTol
validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

Annotations
@Since( "1.4.0" )

Value Members

final def !=(arg0: Any): Boolean

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

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

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

Definition Classes
AnyRef
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def getLearningRate(): Double

Annotations
@Since( "1.2.0" )
def getLoss(): Loss

Annotations
@Since( "1.2.0" )
def getNumIterations(): Int

Annotations
@Since( "1.2.0" )
def getTreeStrategy(): Strategy

Annotations
@Since( "1.2.0" )
def getValidationTol(): Double

Annotations
@Since( "1.4.0" )
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]

Annotations
@Since( "1.2.0" )
var loss: Loss

Loss function used for minimization during gradient boosting.
Loss function used for minimization during gradient boosting.

Annotations
@Since( "1.2.0" )
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.

Annotations
@Since( "1.2.0" )
def setLearningRate(arg0: Double): Unit

Annotations
@Since( "1.2.0" )
def setLoss(arg0: Loss): Unit

Annotations
@Since( "1.2.0" )
def setNumIterations(arg0: Int): Unit

Annotations
@Since( "1.2.0" )
def setTreeStrategy(arg0: Strategy): Unit

Annotations
@Since( "1.2.0" )
def setValidationTol(arg0: Double): Unit

Annotations
@Since( "1.4.0" )
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.

Annotations
@Since( "1.2.0" )
var validationTol: Double

validationTol is a condition which decides iteration termination when runWithValidation is used.
validationTol is a condition which decides iteration termination when runWithValidation is used. The end of iteration is decided based on below logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01. Ignored when org.apache.spark.mllib.tree.GradientBoostedTrees.run() is used.

Annotations
@Since( "1.4.0" )
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object BoostingStrategy | package configuration

case class BoostingStrategy(treeStrategy: Strategy, loss: Loss, numIterations: Int = 100, learningRate: Double = 0.1, validationTol: Double = 0.001) extends Serializable with Product

Instance Constructors

new BoostingStrategy(treeStrategy: Strategy, loss: Loss, numIterations: Int = 100, learningRate: Double = 0.1, validationTol: Double = 0.001)

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

def getValidationTol(): Double

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

def setValidationTol(arg0: Double): Unit

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

var treeStrategy: Strategy

var validationTol: Double

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped