weka.classifiers.trees

Class RandomForest

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.ParallelIteratedSingleClassifierEnhancer

weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

weka.classifiers.meta.Bagging

weka.classifiers.trees.RandomForest

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, Aggregateable<Bagging>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RandomForest
extends Bagging

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:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
 

 -O
  Calculate the out of bag error.
 

 -store-out-of-bag-predictions
  Whether to store out of bag predictions in internal evaluation object.
 

 -output-out-of-bag-complexity-statistics
  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
 

 -print
  Print the individual classifiers in the output
 

 -attribute-importance
  Compute and output attribute importance (mean impurity decrease method)
 

 -I <num>
  Number of iterations (i.e., the number of trees in the random forest).
  (current value 100)
 

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
  (use 0 to auto-detect number of cores)
 

 -K <number of attributes>
  Number of attributes to randomly investigate. (default 0)
  (<1 = int(log_2(#predictors)+1)).
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 1)
 

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 

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

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

 -N <num>
  Number of folds for backfitting (default 0, no backfitting).
 

 -U
  Allow unclassified instances.
 

 -B
  Break ties randomly when several attributes look equally good.
 

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
 

 -batch-size
  The desired batch size for batch prediction  (default 100).
 

Version:

$Revision: 15312 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
RandomForest() Constructor that sets base classifier for bagging to RandomTre and default number of iterations to 100.

Method Summary

Modifier and Type	Method and Description
java.lang.String	breakTiesRandomlyTipText() Returns the tip text for this property
java.lang.String	computeAttributeImportanceTipText() Returns the tip text for this property
double[]	computeAverageImpurityDecreasePerAttribute(double[] nodeCounts) Computes the average impurity decrease per attribute over the trees
boolean	getBreakTiesRandomly() Get whether to break ties randomly.
Capabilities	getCapabilities() Returns default capabilities of the base classifier.
boolean	getComputeAttributeImportance() Get whether to compute and output attribute importance scores
int	getMaxDepth() Get the maximum depth of trh tree, 0 for unlimited.
int	getNumFeatures() Get the number of features used in random selection.
java.lang.String[]	getOptions() Gets the current settings of the forest.
java.lang.String	getRevision() Returns the revision string.
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.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for this class.
java.lang.String	maxDepthTipText() Returns the tip text for this property
java.lang.String	numFeaturesTipText() Returns the tip text for this property
java.lang.String	numIterationsTipText() Returns the tip text for the number of iterations.
void	setBatchSize(java.lang.String size) Set the preferred batch size for batch prediction.
void	setBreakTiesRandomly(boolean newBreakTiesRandomly) Set whether to break ties randomly.
void	setClassifier(Classifier newClassifier) This method only accepts RandomTree arguments.
void	setComputeAttributeImportance(boolean computeAttributeImportance) Set whether to compute and output attribute importance scores
void	setDebug(boolean debug) Set debugging mode.
void	setMaxDepth(int value) Set the maximum depth of the tree, 0 for unlimited.
void	setNumDecimalPlaces(int num) Set the number of decimal places.
void	setNumFeatures(int newNumFeatures) Set the number of features to use in random selection.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRepresentCopiesUsingWeights(boolean representUsingWeights) This method only accepts true as its argument
void	setSeed(int s) Sets the seed for the random number generator.
java.lang.String	toString() Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.meta.Bagging

aggregate, bagSizePercentTipText, batchSizeTipText, buildClassifier, calcOutOfBagTipText, distributionForInstance, distributionsForInstances, enumerateMeasures, finalizeAggregation, generatePartition, getBagSizePercent, getBatchSize, getCalcOutOfBag, getMeasure, getMembershipValues, getOutOfBagEvaluationObject, getOutputOutOfBagComplexityStatistics, getPrintClassifiers, getRepresentCopiesUsingWeights, getStoreOutOfBagPredictions, implementsMoreEfficientBatchPrediction, measureOutOfBagError, numElements, outputOutOfBagComplexityStatisticsTipText, printClassifiersTipText, representCopiesUsingWeightsTipText, setBagSizePercent, setCalcOutOfBag, setOutputOutOfBagComplexityStatistics, setPrintClassifiers, setStoreOutOfBagPredictions, storeOutOfBagPredictionsTipText

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

getSeed, seedTipText

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDoNotCheckCapabilities

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

RandomForest

public RandomForest()

Constructor that sets base classifier for bagging to RandomTre and default number of iterations to 100.

Method Detail

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the base classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of the base classifier

See Also:

Capabilities

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Overrides:

globalInfo in class Bagging

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

Overrides:

getTechnicalInformation in class Bagging

Returns:

the technical information about this class

numIterationsTipText

public java.lang.String numIterationsTipText()

Returns the tip text for the number of iterations. Overridden here to be more informative.

Overrides:

numIterationsTipText in class IteratedSingleClassifierEnhancer

Returns:

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

setClassifier

@ProgrammaticProperty
public void setClassifier(Classifier newClassifier)

This method only accepts RandomTree arguments.

Overrides:

setClassifier in class SingleClassifierEnhancer

Parameters:

newClassifier - the RandomTree to use.

Throws:

if - argument is not a RandomTree

setRepresentCopiesUsingWeights

@ProgrammaticProperty
public void setRepresentCopiesUsingWeights(boolean representUsingWeights)

This method only accepts true as its argument

Overrides:

setRepresentCopiesUsingWeights in class Bagging

Parameters:

representUsingWeights - must be set to true.

Throws:

if - argument is not true

numFeaturesTipText

public java.lang.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.

computeAttributeImportanceTipText

public java.lang.String computeAttributeImportanceTipText()

Returns the tip text for this property

Returns:

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

setComputeAttributeImportance

public void setComputeAttributeImportance(boolean computeAttributeImportance)

Set whether to compute and output attribute importance scores

Parameters:

computeAttributeImportance - true to compute attribute importance scores

getComputeAttributeImportance

public boolean getComputeAttributeImportance()

Get whether to compute and output attribute importance scores

Returns:

true if computing attribute importance scores

maxDepthTipText

public java.lang.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.

breakTiesRandomlyTipText

public java.lang.String breakTiesRandomlyTipText()

Returns the tip text for this property

Returns:

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

getBreakTiesRandomly

public boolean getBreakTiesRandomly()

Get whether to break ties randomly.

Returns:

true if ties are to be broken randomly.

setBreakTiesRandomly

public void setBreakTiesRandomly(boolean newBreakTiesRandomly)

Set whether to break ties randomly.

Parameters:

newBreakTiesRandomly - true if ties are to be broken randomly

setDebug

public void setDebug(boolean debug)

Set debugging mode.

Overrides:

setDebug in class AbstractClassifier

Parameters:

debug - true if debug output should be printed

setNumDecimalPlaces

public void setNumDecimalPlaces(int num)

Set the number of decimal places.

Overrides:

setNumDecimalPlaces in class AbstractClassifier

setBatchSize

public void setBatchSize(java.lang.String size)

Set the preferred batch size for batch prediction.

Specified by:

setBatchSize in interface BatchPredictor

Overrides:

setBatchSize in class Bagging

Parameters:

size - the batch size to use

setSeed

public void setSeed(int s)

Sets the seed for the random number generator.

Specified by:

setSeed in interface Randomizable

Overrides:

setSeed in class RandomizableParallelIteratedSingleClassifierEnhancer

Parameters:

s - the seed to be used

toString

public java.lang.String toString()

Returns description of the bagged classifier.

Overrides:

toString in class Bagging

Returns:

description of the bagged classifier as a string

computeAverageImpurityDecreasePerAttribute

public double[] computeAverageImpurityDecreasePerAttribute(double[] nodeCounts)
                                                    throws WekaException

Computes the average impurity decrease per attribute over the trees

Parameters:

nodeCounts - an optional array that, if non-null, will hold the count of the number of nodes at which each attribute was used for splitting

Returns:

the average impurity decrease per attribute over the trees

Throws:

WekaException

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Bagging

Returns:

an enumeration of all the available options

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the forest.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Bagging

Returns:

an array of strings suitable for passing to setOptions()

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
 

 -O
  Calculate the out of bag error.
 

 -store-out-of-bag-predictions
  Whether to store out of bag predictions in internal evaluation object.
 

 -output-out-of-bag-complexity-statistics
  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
 

 -print
  Print the individual classifiers in the output
 

 -attribute-importance
  Compute and output attribute importance (mean impurity decrease method)
 

 -I <num>
  Number of iterations (i.e., the number of trees in the random forest).
  (current value 100)
 

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
  (use 0 to auto-detect number of cores)
 

 -K <number of attributes>
  Number of attributes to randomly investigate. (default 0)
  (<1 = int(log_2(#predictors)+1)).
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 1)
 

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 

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

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

 -N <num>
  Number of folds for backfitting (default 0, no backfitting).
 

 -U
  Allow unclassified instances.
 

 -B
  Break ties randomly when several attributes look equally good.
 

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
 

 -batch-size
  The desired batch size for batch prediction  (default 100).
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Bagging

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Bagging

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for this class.

Parameters:

argv - the options