smile.classification

Class AdaBoost.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.AdaBoost.Trainer

Enclosing class:

AdaBoost

public static class AdaBoost.Trainer
extends ClassifierTrainer<double[]>

Trainer for AdaBoost classifiers.

Constructor Summary

Constructors

Constructor and Description
Trainer() Default constructor of 500 trees and maximal 2 leaf nodes in the tree.
Trainer(Attribute[] attributes, int T) Constructor.
Trainer(int T) Constructor.

Method Summary

Modifier and Type	Method and Description
AdaBoost.Trainer	setMaximumLeafNodes(int J) Sets the maximum number of leaf nodes in the tree.
AdaBoost.Trainer	setNumTrees(int T) Sets the number of trees in the random forest.
AdaBoost	train(double[][] x, int[] y) Learns a classifier with given training data.

Methods inherited from class smile.classification.ClassifierTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

Trainer

public Trainer()

Default constructor of 500 trees and maximal 2 leaf nodes in the tree.

Trainer

public Trainer(int T)

Constructor.

Parameters:

T - the number of trees.

Trainer

public Trainer(Attribute[] attributes,
               int T)

Constructor.

Parameters:

attributes - the attributes of independent variable.

T - the number of trees.

Method Detail

setNumTrees

public AdaBoost.Trainer setNumTrees(int T)

Sets the number of trees in the random forest.

Parameters:

T - the number of trees.

setMaximumLeafNodes

public AdaBoost.Trainer setMaximumLeafNodes(int J)

Sets the maximum number of leaf nodes in the tree.

Parameters:

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

train

public AdaBoost train(double[][] x,
                      int[] y)

Description copied from class: ClassifierTrainer

Learns a classifier with given training data.

Specified by:

train in class ClassifierTrainer<double[]>

Parameters:

x - the training instances.

y - the training labels.

Returns:

a trained classifier.