jsat.classifiers.trees

Class RandomDecisionTree

java.lang.Object

jsat.classifiers.trees.DecisionTree

jsat.classifiers.trees.RandomDecisionTree

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, TreeLearner, Parameterized, Regressor

public class RandomDecisionTree
extends DecisionTree

An extension of Decision Trees, it ignores the given set of features to use- and selects a new random subset of features at each node for use.

The Random Decision Tree supports missing values in training and prediction.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RandomDecisionTree()
RandomDecisionTree(int numFeatures) Creates a new Random Decision Tree
RandomDecisionTree(int numFeatures, int maxDepth, int minSamples, TreePruner.PruningMethod pruningMethod, double testProportion) Creates a new Random Decision Tree
RandomDecisionTree(RandomDecisionTree toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
RandomDecisionTree	clone()
int	getRandomFeatureCount() Returns the number of random features used at each node of the tree
void	setRandomFeatureCount(int numFeatures) Sets the number of random features to and use at each node of the decision tree

Methods inherited from class jsat.classifiers.trees.DecisionTree

classify, getC45Tree, getGainMethod, getMaxDepth, getMinResultSplitSize, getMinSamples, getParameters, getPruningMethod, getTestProportion, getTreeNodeVisitor, regress, setGainMethod, setMaxDepth, setMinResultSplitSize, setMinSamples, setPruningMethod, setTestProportion, supportsWeightedData, train, train, train, train, trainC

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.regression.Regressor

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter

Constructor Detail

RandomDecisionTree

public RandomDecisionTree()

RandomDecisionTree

public RandomDecisionTree(int numFeatures)

Creates a new Random Decision Tree

Parameters:

numFeatures - the number of random features to use

RandomDecisionTree

public RandomDecisionTree(int numFeatures,
                          int maxDepth,
                          int minSamples,
                          TreePruner.PruningMethod pruningMethod,
                          double testProportion)

Creates a new Random Decision Tree

Parameters:

numFeatures - the number of random features to use

maxDepth - the maximum depth of the tree to create

minSamples - the minimum number of samples needed to continue branching

pruningMethod - the method of pruning to use after construction

testProportion - the proportion of the data set to put aside to use for pruning

RandomDecisionTree

public RandomDecisionTree(RandomDecisionTree toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setRandomFeatureCount

public void setRandomFeatureCount(int numFeatures)

Sets the number of random features to and use at each node of the decision tree

Parameters:

numFeatures - the number of random features

getRandomFeatureCount

public int getRandomFeatureCount()

Returns the number of random features used at each node of the tree

Returns:

the number of random features used at each node of the tree

clone

public RandomDecisionTree clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class DecisionTree