public class RegressionByDiscretization extends SingleClassifierEnhancer
-B <int> Number of bins for equal-width discretization (default 10).
-E Whether to delete empty bins after discretization (default false).
-F Use equal-frequency instead of equal-width discretization.
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U Use unpruned tree.
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of instances> Set minimum number of instances per leaf. (default 2)
-R Use reduced error pruning.
-N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
-B Use binary splits only.
-S Don't perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-Q <seed> Seed for random data shuffling (default 1).
Constructor and Description |
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RegressionByDiscretization()
Default constructor.
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Modifier and Type | Method and Description |
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void |
buildClassifier(Instances instances)
Generates the classifier.
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double |
classifyInstance(Instance instance)
Returns a predicted class for the test instance.
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java.lang.String |
deleteEmptyBinsTipText()
Returns the tip text for this property
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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boolean |
getDeleteEmptyBins()
Gets the number of bins numeric attributes will be divided into
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int |
getNumBins()
Gets the number of bins numeric attributes will be divided into
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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boolean |
getUseEqualFrequency()
Get the value of UseEqualFrequency.
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java.lang.String |
globalInfo()
Returns a string describing classifier
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java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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java.lang.String |
numBinsTipText()
Returns the tip text for this property
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void |
setDeleteEmptyBins(boolean b)
Sets the number of bins to divide each selected numeric attribute into
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void |
setNumBins(int numBins)
Sets the number of bins to divide each selected numeric attribute into
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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void |
setUseEqualFrequency(boolean newUseEqualFrequency)
Set the value of UseEqualFrequency.
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java.lang.String |
toString()
Returns a description of the classifier.
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java.lang.String |
useEqualFrequencyTipText()
Returns the tip text for this property
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classifierTipText, getClassifier, setClassifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
public RegressionByDiscretization()
public java.lang.String globalInfo()
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in class Classifier
instances
- set of instances serving as training datajava.lang.Exception
- if the classifier has not been generated successfullypublic double classifyInstance(Instance instance) throws java.lang.Exception
classifyInstance
in class Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if the prediction couldn't be madepublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class SingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-B <int> Number of bins for equal-width discretization (default 10).
-E Whether to delete empty bins after discretization (default false).
-F Use equal-frequency instead of equal-width discretization.
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U Use unpruned tree.
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of instances> Set minimum number of instances per leaf. (default 2)
-R Use reduced error pruning.
-N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
-B Use binary splits only.
-S Don't perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-Q <seed> Seed for random data shuffling (default 1).
setOptions
in interface OptionHandler
setOptions
in class SingleClassifierEnhancer
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class SingleClassifierEnhancer
public java.lang.String numBinsTipText()
public int getNumBins()
public void setNumBins(int numBins)
numBins
- the number of binspublic java.lang.String deleteEmptyBinsTipText()
public boolean getDeleteEmptyBins()
public void setDeleteEmptyBins(boolean b)
numBins
- the number of binspublic java.lang.String useEqualFrequencyTipText()
public boolean getUseEqualFrequency()
public void setUseEqualFrequency(boolean newUseEqualFrequency)
newUseEqualFrequency
- Value to assign to UseEqualFrequency.public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- the options