jsat.classifiers.trees

Class RandomDecisionTree

java.lang.Object

jsat.classifiers.trees.DecisionTree

jsat.classifiers.trees.RandomDecisionTree

All Implemented Interfaces:

Serializable, 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.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class jsat.classifiers.trees.DecisionTree

DecisionTree.Node

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
protected DecisionTree.Node	makeNodeC(List<DataPointPair<Integer>> dataPoints, Set<Integer> options, int depth, ExecutorService threadPool, ModifiableCountDownLatch mcdl) Makes a new node for classification
protected DecisionTree.Node	makeNodeR(List<DataPointPair<Double>> dataPoints, Set<Integer> options, int depth, ExecutorService threadPool, ModifiableCountDownLatch mcdl) Makes a new node for regression
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, getParameter, getParameters, getPruningMethod, getTestProportion, getTreeNodeVisitor, regress, setGainMethod, setMaxDepth, setMinResultSplitSize, setMinSamples, setPruningMethod, setTestProportion, supportsWeightedData, train, train, train, train, trainC, trainC, trainC, trainC

Methods inherited from class java.lang.Object

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

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

makeNodeC

protected DecisionTree.Node makeNodeC(List<DataPointPair<Integer>> dataPoints,
                                      Set<Integer> options,
                                      int depth,
                                      ExecutorService threadPool,
                                      ModifiableCountDownLatch mcdl)

Description copied from class: DecisionTree

Makes a new node for classification

Overrides:

makeNodeC in class DecisionTree

Parameters:

dataPoints - the list of data points paired with their class

options - the attributes that this tree may select from

depth - the current depth of the tree

threadPool - the source of threads

mcdl - count down latch

Returns:

the node created, or null if no node was created

makeNodeR

protected DecisionTree.Node makeNodeR(List<DataPointPair<Double>> dataPoints,
                                      Set<Integer> options,
                                      int depth,
                                      ExecutorService threadPool,
                                      ModifiableCountDownLatch mcdl)

Description copied from class: DecisionTree

Makes a new node for regression

Overrides:

makeNodeR in class DecisionTree

Parameters:

dataPoints - the list of data points paired with their associated real value

options - the attributes that this tree may select from

depth - the current depth of the tree

threadPool - the source of threads

mcdl - count down latch

Returns:

the node created, or null if no node was created

clone

public RandomDecisionTree clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class DecisionTree