smile.regression

Class GradientTreeBoost.Trainer

java.lang.Object

smile.regression.RegressionTrainer<double[]>

smile.regression.GradientTreeBoost.Trainer

Enclosing class:

GradientTreeBoost

public static class GradientTreeBoost.Trainer
extends RegressionTrainer<double[]>

Trainer for GradientTreeBoost regression.

Constructor Summary

Constructors

Constructor and Description
GradientTreeBoost.Trainer(Attribute[] attributes, int T) Constructor.
GradientTreeBoost.Trainer(int T) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setLoss(GradientTreeBoost.Loss loss) Sets the loss function.
void	setMaximumLeafNodes(int J) Sets the maximum number of leaf nodes in the tree.
void	setNumTrees(int T) Sets the number of trees in the random forest.
void	setSamplingRates(double f) Sets the sampling rate for stochastic tree boosting.
void	setShrinkage(double shrinkage) Sets the shrinkage parameter in (0, 1] controls the learning rate of procedure.
GradientTreeBoost	train(double[][] x, double[] y) Learns a regression model with given training data.

Methods inherited from class smile.regression.RegressionTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

GradientTreeBoost.Trainer

public GradientTreeBoost.Trainer(int T)

Constructor.

Parameters:

T - the number of trees.

GradientTreeBoost.Trainer

public GradientTreeBoost.Trainer(Attribute[] attributes,
                         int T)

Constructor.

Parameters:

attributes - the attributes of independent variable.

T - the number of trees.

Method Detail

setLoss

public void setLoss(GradientTreeBoost.Loss loss)

Sets the loss function.

Parameters:

loss - the loss function.

setNumTrees

public void setNumTrees(int T)

Sets the number of trees in the random forest.

Parameters:

T - the number of trees.

setMaximumLeafNodes

public void setMaximumLeafNodes(int J)

Sets the maximum number of leaf nodes in the tree.

Parameters:

J - the maximum number of leaf nodes in the tree.

setShrinkage

public void setShrinkage(double shrinkage)

Sets the shrinkage parameter in (0, 1] controls the learning rate of procedure.

Parameters:

shrinkage - the learning rate.

setSamplingRates

public void setSamplingRates(double f)

Sets the sampling rate for stochastic tree boosting.

Parameters:

f - the sampling rate for stochastic tree boosting.

train

public GradientTreeBoost train(double[][] x,
                      double[] y)

Description copied from class: RegressionTrainer

Learns a regression model with given training data.

Specified by:

train in class RegressionTrainer<double[]>

Parameters:

x - the training instances.

y - the training response values.

Returns:

a trained regression model.