BalancedRandomForest (Trainable Segmentation 4.0.0 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - ai.BalancedRandomForest

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler
```
public class BalancedRandomForest
extends weka.classifiers.AbstractClassifier
implements weka.core.Randomizable
```
This class implements a Balanced Random Forest classifier, it is an ensemble classifier of random trees where all classes have the same representation in the training process. Class for constructing a balanced 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)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
Author:

Ignacio Arganda-Carreras (iarganda@mit.edu)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

BalancedRandomForest()

Constructors
Constructor and Description
`BalancedRandomForest()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` Build Balanced Random Forest
`double[]`	`distributionForInstance(weka.core.Instance instance)` Calculates the class membership probabilities for the given test instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`int`	`getNumFeatures()` Get the number of random features to use in each node of the random trees.
`int`	`getNumTrees()` Get the number of trees in the forest
`String[]`	`getOptions()` Gets the current settings of the forest.
`int`	`getSeed()` Get the seed used to initialize the random number generators
`weka.core.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
`static void`	`main(String[] argv)` Main method for this class.
`double`	`measureOutOfBagError()` Gets the out of bag error that was calculated as the classifier was built.
`String`	`numFeaturesTipText()` Returns the tip text for this property
`String`	`numTreesTipText()` Returns the tip text for this property
`String`	`seedTipText()` Returns the tip text for this property
`void`	`setNumFeatures(int numFeatures)` Set the number of random features to use in each node of the random trees
`void`	`setNumTrees(int numTrees)` Set the number of trees in the forest
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setSeed(int seed)` Set the seed used to initialize the random number generators
`String`	`toString()` Outputs a description of this classifier.

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - BalancedRandomForest
```
public BalancedRandomForest()
```
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public weka.core.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.
    
    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
  - 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
  - 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
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws Exception
```
    Build Balanced Random Forest
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Throws:
    
    Exception
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the forest.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.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)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getNumFeatures
```
public int getNumFeatures()
```
    Get the number of random features to use in each node of the random trees.
    
    Returns:
    
    number of random features
  - getNumTrees
```
public int getNumTrees()
```
    Get the number of trees in the forest
    
    Returns:
    
    number of trees being used
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - getSeed
```
public int getSeed()
```
    Get the seed used to initialize the random number generators
    
    Specified by:
    
    getSeed in interface weka.core.Randomizable
    
    Returns:
    
    random seed
  - setSeed
```
public void setSeed(int seed)
```
    Set the seed used to initialize the random number generators
    
    Specified by:
    
    setSeed in interface weka.core.Randomizable
    
    Parameters:
    
    seed - random seed
  - setNumTrees
```
public void setNumTrees(int numTrees)
```
    Set the number of trees in the forest
    
    Parameters:
    
    numTrees - number of trees
  - setNumFeatures
```
public void setNumFeatures(int numFeatures)
```
    Set the number of random features to use in each node of the random trees
    
    Parameters:
    
    numFeatures - number of random features
  - measureOutOfBagError
```
public double measureOutOfBagError()
```
    Gets the out of bag error that was calculated as the classifier was built.
    
    Returns:
    
    the out of bag error
  - enumerateMeasures
```
public Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names.
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(String additionalMeasureName)
```
    Returns the value of the named measure.
    
    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
  - toString
```
public String toString()
```
    Outputs a description of this classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a string containing a description of the classifier
  - main
```
public static void main(String[] argv)
```
    Main method for this class.
    
    Parameters:
    
    argv - the options

Class BalancedRandomForest

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

BalancedRandomForest

Method Detail

globalInfo

getTechnicalInformation

numTreesTipText

numFeaturesTipText

seedTipText

buildClassifier

distributionForInstance

getOptions

setOptions

getNumFeatures

getNumTrees

getCapabilities

getSeed

setSeed

setNumTrees

setNumFeatures

measureOutOfBagError

enumerateMeasures

getMeasure

toString

main