weka.classifiers.trees

Class RandomForest

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.trees.RandomForest

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RandomForest
extends AbstractClassifier
implements OptionHandler, Randomizable, WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler

Class for constructing a forest of random trees.

For more information see:

Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

BibTeX:

 @article{Breiman2001,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {1},
    pages = {5-32},
    title = {Random Forests},
    volume = {45},
    year = {2001}
 }
 

Valid options are:

 -I <number of trees>
  Number of trees to build.

 -K <number of features>
  Number of features to consider (<1=int(logM+1)).

 -S
  Seed for random number generator.
  (default 1)

 -depth <num>
  The maximum depth of the trees, 0 for unlimited.
  (default 0)

 -print
  Print the individual trees in the output

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 8892 $

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RandomForest()

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Builds a classifier for a set of instances.
double[]	distributionForInstance(Instance instance) Returns the class probability distribution for an instance.
Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getMaxDepth() Get the maximum depth of trh tree, 0 for unlimited.
double	getMeasure(String additionalMeasureName) Returns the value of the named measure.
int	getNumExecutionSlots() Get the number of execution slots (threads) to use for building the members of the ensemble.
int	getNumFeatures() Get the number of features used in random selection.
int	getNumTrees() Get the value of numTrees.
String[]	getOptions() Gets the current settings of the forest.
boolean	getPrintTrees() Get whether to print the individual ensemble trees in the output
String	getRevision() Returns the revision string.
int	getSeed() Gets the seed for the random number generations
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for this class.
String	maxDepthTipText() Returns the tip text for this property
double	measureOutOfBagError() Gets the out of bag error that was calculated as the classifier was built.
String	numExecutionSlotsTipText() Returns the tip text for this property
String	numFeaturesTipText() Returns the tip text for this property
String	numTreesTipText() Returns the tip text for this property
String	printTreesTipText() Returns the tip text for this property
String	seedTipText() Returns the tip text for this property
void	setMaxDepth(int value) Set the maximum depth of the tree, 0 for unlimited.
void	setNumExecutionSlots(int numSlots) Set the number of execution slots (threads) to use for building the members of the ensemble.
void	setNumFeatures(int newNumFeatures) Set the number of features to use in random selection.
void	setNumTrees(int newNumTrees) Set the value of numTrees.
void	setOptions(String[] options) Parses a given list of options.
void	setPrintTrees(boolean print) Set whether to print the individual ensemble trees in the output
void	setSeed(int seed) Set the seed for random number generation.
String	toString() Outputs a description of this classifier.

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomForest

public RandomForest()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

numTreesTipText

public String numTreesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNumTrees

public int getNumTrees()

Get the value of numTrees.

Returns:

Value of numTrees.

setNumTrees

public void setNumTrees(int newNumTrees)

Set the value of numTrees.

Parameters:

newNumTrees - Value to assign to numTrees.

numFeaturesTipText

public String numFeaturesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNumFeatures

public int getNumFeatures()

Get the number of features used in random selection.

Returns:

Value of numFeatures.

setNumFeatures

public void setNumFeatures(int newNumFeatures)

Set the number of features to use in random selection.

Parameters:

newNumFeatures - Value to assign to numFeatures.

seedTipText

public String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int seed)

Set the seed for random number generation.

Specified by:

setSeed in interface Randomizable

Parameters:

seed - the seed

getSeed

public int getSeed()

Gets the seed for the random number generations

Specified by:

getSeed in interface Randomizable

Returns:

the seed for the random number generation

maxDepthTipText

public String maxDepthTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getMaxDepth

public int getMaxDepth()

Get the maximum depth of trh tree, 0 for unlimited.

Returns:

the maximum depth.

setMaxDepth

public void setMaxDepth(int value)

Set the maximum depth of the tree, 0 for unlimited.

Parameters:

value - the maximum depth.

printTreesTipText

public String printTreesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setPrintTrees

public void setPrintTrees(boolean print)

Set whether to print the individual ensemble trees in the output

Parameters:

print - true if the individual trees are to be printed

getPrintTrees

public boolean getPrintTrees()

Get whether to print the individual ensemble trees in the output

Returns:

true if the individual trees are to be printed

measureOutOfBagError

public double measureOutOfBagError()

Gets the out of bag error that was calculated as the classifier was built.

Returns:

the out of bag error

setNumExecutionSlots

public void setNumExecutionSlots(int numSlots)

Set the number of execution slots (threads) to use for building the members of the ensemble.

Parameters:

numSlots - the number of slots to use.

getNumExecutionSlots

public int getNumExecutionSlots()

Get the number of execution slots (threads) to use for building the members of the ensemble.

Returns:

the number of slots to use

numExecutionSlotsTipText

public String numExecutionSlotsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

enumerateMeasures

public Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names.

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(String additionalMeasureName)

Returns the value of the named measure.

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

IllegalArgumentException - if the named measure is not supported

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClassifier

Returns:

an enumeration of all the available options

getOptions

public String[] getOptions()

Gets the current settings of the forest.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions()

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -I <number of trees>
  Number of trees to build.

 -K <number of features>
  Number of features to consider (<1=int(logM+1)).

 -S
  Seed for random number generator.
  (default 1)

 -depth <num>
  The maximum depth of the trees, 0 for unlimited.
  (default 0)

 -print
  Print the individual trees in the output

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Builds a classifier for a set of instances.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the instances to train the classifier with

Throws:

Exception - if something goes wrong

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Returns the class probability distribution for an instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the distribution the forest generates for the instance

Throws:

Exception - if computation fails

toString

public String toString()

Outputs a description of this classifier.

Overrides:

toString in class Object

Returns:

a string containing a description of the classifier

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for this class.

Parameters:

argv - the options