RandomForest (weka-dev 3.7.10 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.RandomForest

All Implemented Interfaces:: Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

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

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: 9186 $
Author:: Richard Kirkby ([email protected])
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

RandomForest()

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.
`void`	`generatePartition(Instances data)` Builds the classifier to generate a partition.
`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.
`double[]`	`getMembershipValues(Instance inst)` Computes an array that indicates leaf membership
`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.
`int`	`numElements()` Returns the number of elements in the partition.
`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
  - generatePartition
```
public void generatePartition(Instances data)
                       throws Exception
```
    Builds the classifier to generate a partition.
    
    Specified by:
    
    generatePartition in interface PartitionGenerator
    
    Throws:
    
    Exception
  - getMembershipValues
```
public double[] getMembershipValues(Instance inst)
                             throws Exception
```
    Computes an array that indicates leaf membership
    
    Specified by:
    
    getMembershipValues in interface PartitionGenerator
    
    Throws:
    
    Exception
  - numElements
```
public int numElements()
                throws Exception
```
    Returns the number of elements in the partition.
    
    Specified by:
    
    numElements in interface PartitionGenerator
    
    Throws:
    
    Exception
  - 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

Class RandomForest

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Constructor Detail

RandomForest

Method Detail

globalInfo

getTechnicalInformation

numTreesTipText

getNumTrees

setNumTrees

numFeaturesTipText

getNumFeatures

setNumFeatures

seedTipText

setSeed

getSeed

maxDepthTipText

getMaxDepth

setMaxDepth

printTreesTipText

setPrintTrees

getPrintTrees

measureOutOfBagError

setNumExecutionSlots

getNumExecutionSlots

numExecutionSlotsTipText

enumerateMeasures

getMeasure

listOptions

getOptions

setOptions

getCapabilities

buildClassifier

distributionForInstance

toString

generatePartition

getMembershipValues

numElements

getRevision

main