org.apache.spark.mllib.tree.configuration

Class BoostingStrategy

Object

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

All Implemented Interfaces:

java.io.Serializable, scala.Equals, scala.Product

public class BoostingStrategy
extends Object
implements scala.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 > 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 <= 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 and Description
BoostingStrategy(Strategy treeStrategy, Loss loss, int numIterations, double learningRate, double validationTol)

Method Summary

Methods

Modifier and Type	Method and Description
abstract static boolean	canEqual(Object that)
static BoostingStrategy	defaultParams(scala.Enumeration.Value algo) Returns default configuration for the boosting algorithm
static BoostingStrategy	defaultParams(String algo) Returns default configuration for the boosting algorithm
abstract static boolean	equals(Object that)
double	getLearningRate()
Loss	getLoss()
int	getNumIterations()
Strategy	getTreeStrategy()
double	getValidationTol()
double	learningRate()
Loss	loss()
int	numIterations()
abstract static int	productArity()
abstract static Object	productElement(int n)
static scala.collection.Iterator<Object>	productIterator()
static String	productPrefix()
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 Object

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

Methods inherited from interface scala.Product

productArity, productElement, productIterator, productPrefix

Methods inherited from interface scala.Equals

canEqual, equals

Constructor Detail

BoostingStrategy

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

Method Detail

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

canEqual

public abstract static boolean canEqual(Object that)

equals

public abstract static boolean equals(Object that)

productElement

public abstract static Object productElement(int n)

productArity

public abstract static int productArity()

productIterator

public static scala.collection.Iterator<Object> productIterator()

productPrefix

public static String productPrefix()

treeStrategy

public Strategy treeStrategy()

setTreeStrategy

public void setTreeStrategy(Strategy x$1)

loss

public Loss loss()

setLoss

public void setLoss(Loss x$1)

numIterations

public int numIterations()

setNumIterations

public void setNumIterations(int x$1)

learningRate

public double learningRate()

setLearningRate

public void setLearningRate(double x$1)

validationTol

public double validationTol()

setValidationTol

public void setValidationTol(double x$1)

getTreeStrategy

public Strategy getTreeStrategy()

getLoss

public Loss getLoss()

getNumIterations

public int getNumIterations()

getLearningRate

public double getLearningRate()

getValidationTol

public double getValidationTol()