weka.classifiers.meta
Class RegressionByDiscretization

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.meta.RegressionByDiscretization
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler

public class RegressionByDiscretization
extends SingleClassifierEnhancer

A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).

Valid options are:

 -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).

Version:
$Revision: 4746 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
RegressionByDiscretization()
          Default constructor.
 
Method Summary
 void buildClassifier(Instances instances)
          Generates the classifier.
 double classifyInstance(Instance instance)
          Returns a predicted class for the test instance.
 java.lang.String deleteEmptyBinsTipText()
          Returns the tip text for this property
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 boolean getDeleteEmptyBins()
          Gets the number of bins numeric attributes will be divided into
 int getNumBins()
          Gets the number of bins numeric attributes will be divided into
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 java.lang.String getRevision()
          Returns the revision string.
 boolean getUseEqualFrequency()
          Get the value of UseEqualFrequency.
 java.lang.String globalInfo()
          Returns a string describing classifier
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String numBinsTipText()
          Returns the tip text for this property
 void setDeleteEmptyBins(boolean b)
          Sets the number of bins to divide each selected numeric attribute into
 void setNumBins(int numBins)
          Sets the number of bins to divide each selected numeric attribute into
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setUseEqualFrequency(boolean newUseEqualFrequency)
          Set the value of UseEqualFrequency.
 java.lang.String toString()
          Returns a description of the classifier.
 java.lang.String useEqualFrequencyTipText()
          Returns the tip text for this property
 
Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
 
Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

RegressionByDiscretization

public RegressionByDiscretization()
Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing classifier

Returns:
a description suitable for displaying in the explorer/experimenter gui

getCapabilities

public Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class SingleClassifierEnhancer
Returns:
the capabilities of this classifier
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception
Generates the classifier.

Specified by:
buildClassifier in class Classifier
Parameters:
instances - set of instances serving as training data
Throws:
java.lang.Exception - if the classifier has not been generated successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception
Returns a predicted class for the test instance.

Overrides:
classifyInstance in class Classifier
Parameters:
instance - the instance to be classified
Returns:
predicted class value
Throws:
java.lang.Exception - if the prediction couldn't be made

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class SingleClassifierEnhancer
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.

Valid options are:

 -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).

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class SingleClassifierEnhancer
Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()
Gets the current settings of the Classifier.

Specified by:
getOptions in interface OptionHandler
Overrides:
getOptions in class SingleClassifierEnhancer
Returns:
an array of strings suitable for passing to setOptions

numBinsTipText

public java.lang.String numBinsTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getNumBins

public int getNumBins()
Gets the number of bins numeric attributes will be divided into

Returns:
the number of bins.

setNumBins

public void setNumBins(int numBins)
Sets the number of bins to divide each selected numeric attribute into

Parameters:
numBins - the number of bins

deleteEmptyBinsTipText

public java.lang.String deleteEmptyBinsTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getDeleteEmptyBins

public boolean getDeleteEmptyBins()
Gets the number of bins numeric attributes will be divided into

Returns:
the number of bins.

setDeleteEmptyBins

public void setDeleteEmptyBins(boolean b)
Sets the number of bins to divide each selected numeric attribute into

Parameters:
numBins - the number of bins

useEqualFrequencyTipText

public java.lang.String useEqualFrequencyTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getUseEqualFrequency

public boolean getUseEqualFrequency()
Get the value of UseEqualFrequency.

Returns:
Value of UseEqualFrequency.

setUseEqualFrequency

public void setUseEqualFrequency(boolean newUseEqualFrequency)
Set the value of UseEqualFrequency.

Parameters:
newUseEqualFrequency - Value to assign to UseEqualFrequency.

toString

public java.lang.String toString()
Returns a description of the classifier.

Overrides:
toString in class java.lang.Object
Returns:
a description of the classifier as a string.

getRevision

public java.lang.String getRevision()
Returns the revision string.

Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options