IterativeClassifierOptimizer (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.meta.IterativeClassifierOptimizer

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler

public class IterativeClassifierOptimizer
extends RandomizableClassifier
implements AdditionalMeasureProducer

Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation. Optimizes the number of iterations of the given iterative classifier using cross-validation or a percentage split evaluation.

Valid options are:

 -A
  If set, average estimate is used rather than one estimate from pooled predictions.

 -L <num>
  The number of iterations to look ahead for to find a better optimum.
  (default 50)

 -P <int>
  The size of the thread pool, for example, the number of cores in the CPU.
  (default 1)

 -E <int>
  The number of threads to use, which should be >= size of thread pool.
  (default 1)

 -I <num>
  Step size for the evaluation, if evaluation is time consuming.
  (default 1)

 -F <num>
  Number of folds for cross-validation.
  (default 10)

 -R <num>
  Number of runs for cross-validation.
  (default 1)

 -W
  Full name of base classifier.
  (default: weka.classifiers.meta.LogitBoost)

 -metric <name>
  Evaluation metric to optimise (default rmse). Available metrics:
  correct,incorrect,kappa,total cost,average cost,kb relative,kb information,
  correlation,complexity 0,complexity scheme,complexity improvement,
  mae,rmse,rae,rrse,coverage,region size,tp rate,fp rate,precision,recall,
  f-measure,mcc,roc area,prc area

 -class-value-index <0-based index>
  Class value index to optimise. Ignored for all but information-retrieval
  type metrics (such as roc area). If unspecified (or a negative value is supplied),
  and an information-retrieval metric is specified, then the class-weighted average
  metric used. (default -1)

 -S <num>
  Random number seed.
  (default 1)

 -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).

 
 Options specific to classifier weka.classifiers.meta.LogitBoost:

 -Q
  Use resampling instead of reweighting for boosting.

 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)

 -H <num>
  Shrinkage parameter.
  (default 1)

 -Z <num>
  Z max threshold for responses.
  (default 3)

 -O <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)

 -E <int>
  The number of threads to use for batch prediction, which should be >= size of thread pool.
  (default 1)

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -percentage <num>
  The percentage of data to be used for training (if 0, k-fold cross-validation is used).
  (default 0)

 -order
  Whether to preserve order when a percentage split evaluation is performed.

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 -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).

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:

 -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).

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`m_bestNumIts` The best number of iterations identified.
`protected double`	`m_bestResult` The best value found for the criterion to be optimized.
`protected int`	`m_classValueIndex` The class value index to use with information retrieval type metrics. < 0 indicates to use the class weighted average version of the metric".
`protected java.lang.String`	`m_evalMetric` The evaluation metric to use
`protected IterativeClassifier`	`m_IterativeClassifier` The base classifier to use
`protected int`	`m_lookAheadIterations` The number of iterations to look ahead for to find a better optimum.
`protected int`	`m_NumFolds` The number of folds for the cross-validation.
`protected int`	`m_NumRuns` The number of runs for the cross-validation.
`protected int`	`m_numThreads` The number of threads to use for parallel building of classifiers.
`protected int`	`m_poolSize` The size of the thread pool.
`protected boolean`	`m_preserveOrderInPercentageSplitEvaluation` Whether to preserve order when a percentage split evaluation is performed.
`protected double`	`m_splitPercentage` The percentage of data to be used for training (if 0, k-fold cross-validation is used).
`protected int`	`m_StepSize` The steps size determining when evaluations happen.
`protected double[]`	`m_thresholds` The thresholds to be used for classification, if the metric implements ThresholdProducingMetric.
`protected boolean`	`m_UseAverage` Whether to use average.
`static Tag[]`	`TAGS_EVAL`

Fields inherited from class weka.classifiers.RandomizableClassifier
m_Seed

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

IterativeClassifierOptimizer()

Constructors
Constructor and Description
`IterativeClassifierOptimizer()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Builds the classifier.
`java.lang.String`	`classValueIndexTipText()` Returns the tip text for this property
`protected java.lang.String`	`defaultIterativeClassifierString()` String describing default classifier.
`double[]`	`distributionForInstance(Instance inst)` Returns the class distribution for an instance.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`evaluationMetricTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the base classifier.
`int`	`getClassValueIndex()` Get the class value index to use
`SelectedTag`	`getEvaluationMetric()` Get the evaluation metric to use
`IterativeClassifier`	`getIterativeClassifier()` Get the classifier used as the base learner.
`protected IterativeClassifier`	`getIterativeClassifier(java.lang.String name, java.lang.String[] options)` Get classifier for string.
`protected java.lang.String`	`getIterativeClassifierSpec()` Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
`int`	`getLookAheadIterations()` Get the value of LookAheadIterations.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`int`	`getNumFolds()` Get the value of NumFolds.
`int`	`getNumRuns()` Get the value of NumRuns.
`int`	`getNumThreads()` Gets the number of threads.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`int`	`getPoolSize()` Gets the number of threads.
`boolean`	`getPreserveOrderInPercentageSplitEvaluation()` Get the value of PreserveOrderInPercentageSplitEvaluation.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSplitPercentage()` Get the value of SplitPercentage.
`int`	`getStepSize()` Get the value of StepSize.
`boolean`	`getUseAverage()` Get the value of UseAverage.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`iterativeClassifierTipText()` Returns the tip text for this property
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`lookAheadIterationsTipText()` Returns the tip text for this property
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureBestNumIts()` Returns the best number of iterations
`double`	`measureBestVal()` Returns the measure for the best model
`java.lang.String`	`numFoldsTipText()` Returns the tip text for this property
`java.lang.String`	`numRunsTipText()` Returns the tip text for this property
`java.lang.String`	`numThreadsTipText()`
`java.lang.String`	`poolSizeTipText()`
`java.lang.String`	`preserveOrderInPercentageSplitEvaluationTipText()` Returns the tip text for this property
`void`	`setClassValueIndex(int i)` Set the class value index to use
`void`	`setEvaluationMetric(SelectedTag metric)` Set the evaluation metric to use
`void`	`setIterativeClassifier(IterativeClassifier newIterativeClassifier)` Set the base learner.
`void`	`setLookAheadIterations(int newLookAheadIterations)` Set the value of LookAheadIterations.
`void`	`setNumFolds(int newNumFolds)` Set the value of NumFolds.
`void`	`setNumRuns(int newNumRuns)` Set the value of NumRuns.
`void`	`setNumThreads(int nT)` Sets the number of threads
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPoolSize(int nT)` Sets the number of threads
`void`	`setPreserveOrderInPercentageSplitEvaluation(boolean newPreserveOrderInPercentageSplitEvaluation)` Set the value of PreserveOrderInPercentageSplitEvaluation.
`void`	`setSplitPercentage(double newSplitPercentage)` Set the value of SplitPercentage.
`void`	`setStepSize(int newStepSize)` Set the value of StepSize.
`void`	`setUseAverage(boolean newUseAverage)` Set the value of UseAverage.
`java.lang.String`	`splitPercentageTipText()` Returns the tip text for this property
`java.lang.String`	`stepSizeTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Returns a string describing the classifier.
`java.lang.String`	`useAverageTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableClassifier
getSeed, seedTipText, setSeed

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

- Field Detail
  - m_IterativeClassifier
```
protected IterativeClassifier m_IterativeClassifier
```
    The base classifier to use
  - m_NumFolds
```
protected int m_NumFolds
```
    The number of folds for the cross-validation.
  - m_NumRuns
```
protected int m_NumRuns
```
    The number of runs for the cross-validation.
  - m_StepSize
```
protected int m_StepSize
```
    The steps size determining when evaluations happen.
  - m_UseAverage
```
protected boolean m_UseAverage
```
    Whether to use average.
  - m_lookAheadIterations
```
protected int m_lookAheadIterations
```
    The number of iterations to look ahead for to find a better optimum.
  - TAGS_EVAL
```
public static Tag[] TAGS_EVAL
```
  - m_evalMetric
```
protected java.lang.String m_evalMetric
```
    The evaluation metric to use
  - m_classValueIndex
```
protected int m_classValueIndex
```
    The class value index to use with information retrieval type metrics. < 0 indicates to use the class weighted average version of the metric".
  - m_thresholds
```
protected double[] m_thresholds
```
    The thresholds to be used for classification, if the metric implements ThresholdProducingMetric.
  - m_bestResult
```
protected double m_bestResult
```
    The best value found for the criterion to be optimized.
  - m_bestNumIts
```
protected int m_bestNumIts
```
    The best number of iterations identified.
  - m_numThreads
```
protected int m_numThreads
```
    The number of threads to use for parallel building of classifiers.
  - m_poolSize
```
protected int m_poolSize
```
    The size of the thread pool.
  - m_splitPercentage
```
protected double m_splitPercentage
```
    The percentage of data to be used for training (if 0, k-fold cross-validation is used).
  - m_preserveOrderInPercentageSplitEvaluation
```
protected boolean m_preserveOrderInPercentageSplitEvaluation
```
    Whether to preserve order when a percentage split evaluation is performed.
- Constructor Detail
  - IterativeClassifierOptimizer
```
public IterativeClassifierOptimizer()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - defaultIterativeClassifierString
```
protected java.lang.String defaultIterativeClassifierString()
```
    String describing default classifier.
  - useAverageTipText
```
public java.lang.String useAverageTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUseAverage
```
public boolean getUseAverage()
```
    Get the value of UseAverage.
    
    Returns:
    
    Value of UseAverage.
  - setUseAverage
```
public void setUseAverage(boolean newUseAverage)
```
    Set the value of UseAverage.
    
    Parameters:
    
    newUseAverage - Value to assign to UseAverage.
  - numThreadsTipText
```
public java.lang.String numThreadsTipText()
```
    Returns:
    
    a string to describe the option
  - getNumThreads
```
public int getNumThreads()
```
    Gets the number of threads.
  - setNumThreads
```
public void setNumThreads(int nT)
```
    Sets the number of threads
  - poolSizeTipText
```
public java.lang.String poolSizeTipText()
```
    Returns:
    
    a string to describe the option
  - getPoolSize
```
public int getPoolSize()
```
    Gets the number of threads.
  - setPoolSize
```
public void setPoolSize(int nT)
```
    Sets the number of threads
  - stepSizeTipText
```
public java.lang.String stepSizeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getStepSize
```
public int getStepSize()
```
    Get the value of StepSize.
    
    Returns:
    
    Value of StepSize.
  - setStepSize
```
public void setStepSize(int newStepSize)
```
    Set the value of StepSize.
    
    Parameters:
    
    newStepSize - Value to assign to StepSize.
  - numRunsTipText
```
public java.lang.String numRunsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumRuns
```
public int getNumRuns()
```
    Get the value of NumRuns.
    
    Returns:
    
    Value of NumRuns.
  - setNumRuns
```
public void setNumRuns(int newNumRuns)
```
    Set the value of NumRuns.
    
    Parameters:
    
    newNumRuns - Value to assign to NumRuns.
  - numFoldsTipText
```
public java.lang.String numFoldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumFolds
```
public int getNumFolds()
```
    Get the value of NumFolds.
    
    Returns:
    
    Value of NumFolds.
  - setNumFolds
```
public void setNumFolds(int newNumFolds)
```
    Set the value of NumFolds.
    
    Parameters:
    
    newNumFolds - Value to assign to NumFolds.
  - lookAheadIterationsTipText
```
public java.lang.String lookAheadIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLookAheadIterations
```
public int getLookAheadIterations()
```
    Get the value of LookAheadIterations.
    
    Returns:
    
    Value of LookAheadIterations.
  - setLookAheadIterations
```
public void setLookAheadIterations(int newLookAheadIterations)
```
    Set the value of LookAheadIterations.
    
    Parameters:
    
    newLookAheadIterations - Value to assign to LookAheadIterations.
  - splitPercentageTipText
```
public java.lang.String splitPercentageTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSplitPercentage
```
public double getSplitPercentage()
```
    Get the value of SplitPercentage.
    
    Returns:
    
    Value of SplitPercentage.
  - setSplitPercentage
```
public void setSplitPercentage(double newSplitPercentage)
```
    Set the value of SplitPercentage.
    
    Parameters:
    
    newSplitPercentage - Value to assign to SplitPercentage.
  - preserveOrderInPercentageSplitEvaluationTipText
```
public java.lang.String preserveOrderInPercentageSplitEvaluationTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getPreserveOrderInPercentageSplitEvaluation
```
public boolean getPreserveOrderInPercentageSplitEvaluation()
```
    Get the value of PreserveOrderInPercentageSplitEvaluation.
    
    Returns:
    
    Value of PreserveOrderInPercentageSplitEvaluation.
  - setPreserveOrderInPercentageSplitEvaluation
```
public void setPreserveOrderInPercentageSplitEvaluation(boolean newPreserveOrderInPercentageSplitEvaluation)
```
    Set the value of PreserveOrderInPercentageSplitEvaluation.
    
    Parameters:
    
    newPreserveOrderInPercentageSplitEvaluation - Value to assign to PreserveOrderInPercentageSplitEvaluation.
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Builds the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Returns the class distribution for an instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction
    
    Throws:
    
    java.lang.Exception - if distribution could not be computed successfully
  - toString
```
public java.lang.String toString()
```
    Returns a string describing the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options. Options after -- are passed to the designated classifier.
    
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getIterativeClassifier
```
protected IterativeClassifier getIterativeClassifier(java.lang.String name,
                                                     java.lang.String[] options)
                                              throws java.lang.Exception
```
    Get classifier for string.
    
    Returns:
    
    a classifier
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - evaluationMetricTipText
```
public java.lang.String evaluationMetricTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEvaluationMetric
```
public void setEvaluationMetric(SelectedTag metric)
```
    Set the evaluation metric to use
    
    Parameters:
    
    metric - the metric to use
  - getEvaluationMetric
```
public SelectedTag getEvaluationMetric()
```
    Get the evaluation metric to use
    
    Returns:
    
    the evaluation metric to use
  - classValueIndexTipText
```
public java.lang.String classValueIndexTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setClassValueIndex
```
public void setClassValueIndex(int i)
```
    Set the class value index to use
    
    Parameters:
    
    i - the class value index to use
  - getClassValueIndex
```
public int getClassValueIndex()
```
    Get the class value index to use
    
    Returns:
    
    the class value index to use
  - iterativeClassifierTipText
```
public java.lang.String iterativeClassifierTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - 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 AbstractClassifier
    
    Returns:
    
    the capabilities of the base classifier
    
    See Also:
    
    Capabilities
  - setIterativeClassifier
```
public void setIterativeClassifier(IterativeClassifier newIterativeClassifier)
```
    Set the base learner.
    
    Parameters:
    
    newIterativeClassifier - the classifier to use.
  - getIterativeClassifier
```
public IterativeClassifier getIterativeClassifier()
```
    Get the classifier used as the base learner.
    
    Returns:
    
    the classifier used as the classifier
  - getIterativeClassifierSpec
```
protected java.lang.String getIterativeClassifierSpec()
```
    Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
    
    Returns:
    
    the classifier string
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - measureBestNumIts
```
public double measureBestNumIts()
```
    Returns the best number of iterations
    
    Returns:
    
    the best number of iterations
  - measureBestVal
```
public double measureBestVal()
```
    Returns the measure for the best model
    
    Returns:
    
    the number of leaves
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> 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(java.lang.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:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class IterativeClassifierOptimizer

Field Summary

Fields inherited from class weka.classifiers.RandomizableClassifier

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_IterativeClassifier

m_NumFolds

m_NumRuns

m_StepSize

m_UseAverage

m_lookAheadIterations

TAGS_EVAL

m_evalMetric

m_classValueIndex

m_thresholds

m_bestResult

m_bestNumIts

m_numThreads

m_poolSize

m_splitPercentage

m_preserveOrderInPercentageSplitEvaluation

Constructor Detail

IterativeClassifierOptimizer

Method Detail

globalInfo

defaultIterativeClassifierString

useAverageTipText

getUseAverage

setUseAverage

numThreadsTipText

getNumThreads

setNumThreads

poolSizeTipText

getPoolSize

setPoolSize

stepSizeTipText

getStepSize

setStepSize

numRunsTipText

getNumRuns

setNumRuns

numFoldsTipText

getNumFolds

setNumFolds

lookAheadIterationsTipText

getLookAheadIterations

setLookAheadIterations

splitPercentageTipText

getSplitPercentage

setSplitPercentage

preserveOrderInPercentageSplitEvaluationTipText

getPreserveOrderInPercentageSplitEvaluation

setPreserveOrderInPercentageSplitEvaluation

buildClassifier

distributionForInstance

toString

listOptions

setOptions

getIterativeClassifier

getOptions

evaluationMetricTipText

setEvaluationMetric

getEvaluationMetric

classValueIndexTipText

setClassValueIndex

getClassValueIndex

iterativeClassifierTipText

getCapabilities

setIterativeClassifier

getIterativeClassifier

getIterativeClassifierSpec

getRevision

measureBestNumIts

measureBestVal

enumerateMeasures