weka.classifiers.meta
Class RegressionByDiscretization

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.meta.RegressionByDiscretization

All Implemented Interfaces:

Serializable, Cloneable, Classifier, ConditionalDensityEstimator, IntervalEstimator, CapabilitiesHandler, OptionHandler, RevisionHandler

public class RegressionByDiscretization
extends SingleClassifierEnhancer
implements IntervalEstimator, ConditionalDensityEstimator

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: 8034 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static int	ESTIMATOR_HISTOGRAM Use histogram estimator
static int	ESTIMATOR_KERNEL filter: Standardize training data
static int	ESTIMATOR_NORMAL filter: No normalization/standardization
static Tag[]	TAGS_ESTIMATOR The filter to apply to the training data

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.
String	deleteEmptyBinsTipText() Returns the tip text for this property
String	estimatorTypeTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getDeleteEmptyBins() Gets whether empty bins are deleted.
SelectedTag	getEstimatorType() Get the estimator type
boolean	getMinimizeAbsoluteError() Gets whether to min.
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.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
boolean	getUseEqualFrequency() Get the value of UseEqualFrequency.
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
double	logDensity(Instance instance, double value) Returns natural logarithm of density estimate for given value based on given instance.
static void	main(String[] argv) Main method for testing this class.
String	minimizeAbsoluteErrorTipText() Returns the tip text for this property
String	numBinsTipText() Returns the tip text for this property
double[][]	predictIntervals(Instance instance, double confidenceLevel) Returns an N * 2 array, where N is the number of prediction intervals.
void	setDeleteEmptyBins(boolean b) Sets whether to delete empty bins.
void	setEstimatorType(SelectedTag newEstimator) Set the estimator
void	setMinimizeAbsoluteError(boolean b) Sets whether to min.
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.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

ESTIMATOR_HISTOGRAM

public static final int ESTIMATOR_HISTOGRAM

Use histogram estimator

See Also:

Constant Field Values

ESTIMATOR_KERNEL

public static final int ESTIMATOR_KERNEL

filter: Standardize training data

See Also:

Constant Field Values

ESTIMATOR_NORMAL

public static final int ESTIMATOR_NORMAL

filter: No normalization/standardization

See Also:

Constant Field Values

TAGS_ESTIMATOR

public static final Tag[] TAGS_ESTIMATOR

The filter to apply to the training data

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

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface 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 interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

predictIntervals

public double[][] predictIntervals(Instance instance,
                                   double confidenceLevel)
                            throws Exception

Returns an N * 2 array, where N is the number of prediction intervals. In each row, the first element contains the lower boundary of the corresponding prediction interval and the second element the upper boundary.

Specified by:

predictIntervals in interface IntervalEstimator

Parameters:

inst - the instance to make the prediction for.

confidenceLevel - the percentage of cases that the interval should cover.

Returns:

an array of prediction intervals

Throws:

Exception - if the intervals can't be computed

logDensity

public double logDensity(Instance instance,
                         double value)
                  throws Exception

Returns natural logarithm of density estimate for given value based on given instance.

Specified by:

logDensity in interface ConditionalDensityEstimator

Parameters:

inst - the instance to make the prediction for.

the - value to make the prediction for.

Returns:

the natural logarithm of the density estimate

Throws:

Exception - if the intervals can't be computed

classifyInstance

public double classifyInstance(Instance instance)
                        throws Exception

Returns a predicted class for the test instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

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.

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 whether empty bins are deleted.

Returns:

true if empty bins get deleted.

setDeleteEmptyBins

public void setDeleteEmptyBins(boolean b)

Sets whether to delete empty bins.

Parameters:

b - if true, empty bins will be deleted

minimizeAbsoluteErrorTipText

public String minimizeAbsoluteErrorTipText()

Returns the tip text for this property

Returns:

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

getMinimizeAbsoluteError

public boolean getMinimizeAbsoluteError()

Gets whether to min. abs. error

Returns:

true if abs. err. is to be minimized

setMinimizeAbsoluteError

public void setMinimizeAbsoluteError(boolean b)

Sets whether to min. abs. error.

Parameters:

b - if true, abs. err. is minimized

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.

estimatorTypeTipText

public String estimatorTypeTipText()

Returns the tip text for this property

Returns:

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

getEstimatorType

public SelectedTag getEstimatorType()

Get the estimator type

Returns:

the estimator type

setEstimatorType

public void setEstimatorType(SelectedTag newEstimator)

Set the estimator

Parameters:

newEstimator - the estimator to use

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 AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options