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java.lang.Object | +----weka.attributeSelection.ASEvaluation | +----weka.attributeSelection.SubsetEvaluator | +----weka.attributeSelection.HoldOutSubsetEvaluator | +----weka.attributeSelection.ClassifierSubsetEval
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Class name of the classifier to use for accuracy estimation.
Place any classifier options last on the command line following a
"--". Eg -B weka.classifiers.NaiveBayes ... -- -K
Use the training data for accuracy estimation rather than a hold out/
test set.
The file containing hold out/test instances to use for accuracy estimation
buildEvaluator(Instances)
classifierTipText()
evaluateSubset(BitSet)
evaluateSubset(BitSet, Instance, boolean)
evaluateSubset(BitSet, Instances)
getClassifier()
getHoldOutFile()
getOptions()
getUseTraining()
globalInfo()
holdOutFileTipText()
listOptions()
Class name of the classifier to use for accuracy estimation.
main(String[])
setClassifier(Classifier)
setHoldOutFile(File)
setOptions(String[])
setUseTraining(boolean)
toString()
useTrainingTipText()
ClassifierSubsetEval
public ClassifierSubsetEval()
globalInfo
public java.lang.String globalInfo()
Returns a string describing this attribute evaluator
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
Class name of the classifier to use for accuracy estimation.
Place any classifier options last on the command line following a
"--". Eg -B weka.classifiers.NaiveBayes ... -- -K
Use the training data for accuracy estimation rather than a hold out/
test set.
The file containing hold out/test instances to use for accuracy estimation
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
Parses a given list of options.
Valid options are:
Class name of classifier to use for accuracy estimation.
Place any classifier options last on the command line following a
"--". Eg -B weka.classifiers.NaiveBayes ... -- -K
Use training data instead of a hold out/test set for accuracy estimation.
Name of the hold out/test set to estimate classifier accuracy on.
options
- the list of options as an array of strings
classifierTipText
public java.lang.String classifierTipText()
Returns the tip text for this property
setClassifier
public void setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimation
newClassifier
- the Classifier to use.
getClassifier
public Classifier getClassifier()
Get the classifier used as the base learner.
holdOutFileTipText
public java.lang.String holdOutFileTipText()
Returns the tip text for this property
getHoldOutFile
public java.io.File getHoldOutFile()
Gets the file that holds hold out/test instances.
setHoldOutFile
public void setHoldOutFile(java.io.File h)
Set the file that contains hold out/test instances
h
- the hold out file
useTrainingTipText
public java.lang.String useTrainingTipText()
Returns the tip text for this property
getUseTraining
public boolean getUseTraining()
Get if training data is to be used instead of hold out/test data
setUseTraining
public void setUseTraining(boolean t)
Set if training data is to be used instead of hold out/test data
getOptions
public java.lang.String[] getOptions()
Gets the current settings of ClassifierSubsetEval
buildEvaluator
public void buildEvaluator(Instances data) throws java.lang.Exception
Generates a attribute evaluator. Has to initialize all fields of the
evaluator that are not being set via options.
data
- set of instances serving as training data
evaluateSubset
public double evaluateSubset(java.util.BitSet subset) throws java.lang.Exception
Evaluates a subset of attributes
subset
- a bitset representing the attribute subset to be
evaluated
evaluateSubset
public double evaluateSubset(java.util.BitSet subset,
Instances holdOut) throws java.lang.Exception
Evaluates a subset of attributes with respect to a set of instances.
Calling this function overides any test/hold out instancs set from
setHoldOutFile.
subset
- a bitset representing the attribute subset to be
evaluated
holdOut
- a set of instances (possibly seperate and distinct
from those use to build/train the evaluator) with which to
evaluate the merit of the subset
evaluateSubset
public double evaluateSubset(java.util.BitSet subset,
Instance holdOut,
boolean retrain) throws java.lang.Exception
Evaluates a subset of attributes with respect to a single instance.
Calling this function overides any hold out/test instances set
through setHoldOutFile.
subset
- a bitset representing the attribute subset to be
evaluated
holdOut
- a single instance (possibly not one of those used to
build/train the evaluator) with which to evaluate the merit of the subset
retrain
- true if the classifier should be retrained with respect
to the new subset before testing on the holdOut instance.
toString
public java.lang.String toString()
Returns a string describing classifierSubsetEval
main
public static void main(java.lang.String args[])
Main method for testing this class.
args
- the options
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