smile.classification

Class RandomForest.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.RandomForest.Trainer

Enclosing class:

RandomForest

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

Trainer for random forest classifiers.

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
void	setNumRandomFeatures(int M) Sets the number of random selected features for splitting.
void	setNumTrees(int T) Sets the number of trees in the random forest.
RandomForest	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

RandomForest.Trainer

public RandomForest.Trainer(int T)

Constructor.

Parameters:

T - the number of trees.

RandomForest.Trainer

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

Constructor.

Parameters:

attributes - the attributes of independent variable.

T - the number of trees.

Method Detail

setNumTrees

public void setNumTrees(int T)

Sets the number of trees in the random forest.

Parameters:

T - the number of trees.

setNumRandomFeatures

public void setNumRandomFeatures(int M)

Sets the number of random selected features for splitting.

Parameters:

M - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

train

public RandomForest 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.