weka.classifiers.meta

Class RegressionByDiscretization

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.RegressionByDiscretization

All Implemented Interfaces:

Serializable, 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 ([email protected]), Eibe Frank ([email protected])

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RegressionByDiscretization() Default constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Returns a predicted class for the test instance.
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
String[]	getOptions() Gets the current settings of the Classifier.
String	getRevision() Returns the revision string.
boolean	getUseEqualFrequency() Get the value of UseEqualFrequency.
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
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(String[] options) Parses a given list of options.
void	setUseEqualFrequency(boolean newUseEqualFrequency) Set the value of UseEqualFrequency.
String	toString() Returns a description of the classifier.
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 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 Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws 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:

Exception - if the prediction couldn't be made

listOptions

public 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(String[] options)
                throws 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:

Exception - if an option is not supported

getOptions

public 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 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 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 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 String toString()

Returns a description of the classifier.

Overrides:

toString in class Object

Returns:

a description of the classifier as a string.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options