weka.classifiers.meta
Class RegressionByDiscretization

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.SingleClassifierEnhancer
              |
              +--weka.classifiers.meta.RegressionByDiscretization

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class RegressionByDiscretization

extends SingleClassifierEnhancer

Class for a regression scheme that employs any distribution 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:

-D
Produce debugging output.

-B
Number of bins for equal-width discretization (default 10).

Version:

$Revision: 1.29 $

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

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

debugTipText, distributionForInstance, forName, getDebug, makeCopies, 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

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:

-D
Produce debugging output.

-W classifierstring
Classifierstring should contain the full class name of a classifier followed by options to the classifier (default: weka.classifiers.rules.ZeroR).

-B
Number of bins for equal-width discretization (default 10).

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

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.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options