Package org.apache.spark.mllib.tree.configuration

Class BoostingStrategy

Object

org.apache.spark.mllib.tree.configuration.BoostingStrategy

All Implemented Interfaces:

Serializable, scala.Equals, scala.Product

public class BoostingStrategy
extends Object
implements Serializable, scala.Product

Configuration options for GradientBoostedTrees.

param: treeStrategy Parameters for the tree algorithm. We support regression and binary classification for boosting. Impurity setting will be ignored. param: loss Loss function used for minimization during gradient boosting. param: numIterations Number of iterations of boosting. In other words, the number of weak hypotheses used in the final model. param: learningRate Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1] param: 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.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
BoostingStrategy(Strategy treeStrategy, Loss loss, int numIterations, double learningRate, double validationTol)

Method Summary

Modifier and Type	Method	Description
static BoostingStrategy	defaultParams(String algo)	Returns default configuration for the boosting algorithm
static BoostingStrategy	defaultParams(scala.Enumeration.Value algo)	Returns default configuration for the boosting algorithm
double	getLearningRate()
Loss	getLoss()
int	getNumIterations()
Strategy	getTreeStrategy()
double	getValidationTol()
double	learningRate()
Loss	loss()
int	numIterations()
void	setLearningRate(double x$1)
void	setLoss(Loss x$1)
void	setNumIterations(int x$1)
void	setTreeStrategy(Strategy x$1)
void	setValidationTol(double x$1)
Strategy	treeStrategy()
double	validationTol()

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface scala.Equals

canEqual, equals

Methods inherited from interface scala.Product

productArity, productElement, productElementName, productElementNames, productIterator, productPrefix

Constructor Details

BoostingStrategy

public BoostingStrategy(Strategy treeStrategy,
 Loss loss,
 int numIterations,
 double learningRate,
 double validationTol)

Method Details

defaultParams

public static BoostingStrategy defaultParams(String algo)

Returns default configuration for the boosting algorithm

Parameters:

algo - Learning goal. Supported: "Classification" or "Regression"

Returns:

Configuration for boosting algorithm

defaultParams

public static BoostingStrategy defaultParams(scala.Enumeration.Value algo)

Returns default configuration for the boosting algorithm

Parameters:

algo - Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression

Returns:

Configuration for boosting algorithm

treeStrategy

public Strategy treeStrategy()

loss

public Loss loss()

numIterations

public int numIterations()

learningRate

public double learningRate()

validationTol

public double validationTol()

getLearningRate

public double getLearningRate()

getLoss

public Loss getLoss()

getNumIterations

public int getNumIterations()

getTreeStrategy

public Strategy getTreeStrategy()

getValidationTol

public double getValidationTol()

setLearningRate

public void setLearningRate(double x$1)

setLoss

public void setLoss(Loss x$1)

setNumIterations

public void setNumIterations(int x$1)

setTreeStrategy

public void setTreeStrategy(Strategy x$1)

setValidationTol

public void setValidationTol(double x$1)