smile.classification

Class DecisionTree.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.DecisionTree.Trainer

Enclosing class:

DecisionTree

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

Trainer for decision tree classifiers.

Constructor Summary

Constructors

Constructor and Description
Trainer() Default constructor of maximal 100 leaf nodes in the tree.
Trainer(Attribute[] attributes, int J) Constructor.
Trainer(int J) Constructor.

Method Summary

Modifier and Type	Method and Description
DecisionTree.Trainer	setMaximumLeafNodes(int J) Sets the maximum number of leaf nodes in the tree.
DecisionTree.Trainer	setSplitRule(DecisionTree.SplitRule rule) Sets the splitting rule.
DecisionTree	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 maximal 100 leaf nodes in the tree.

Trainer

public Trainer(int J)

Constructor.

Parameters:

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

Trainer

public Trainer(Attribute[] attributes,
               int J)

Constructor.

Parameters:

attributes - the attributes of independent variable.

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

Method Detail

setSplitRule

public DecisionTree.Trainer setSplitRule(DecisionTree.SplitRule rule)

Sets the splitting rule.

Parameters:

rule - the splitting rule.

setMaximumLeafNodes

public DecisionTree.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 DecisionTree 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.