RegressionByDiscretization (weka-stable 3.6.14 API)

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 (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

RegressionByDiscretization()
Default constructor.

Constructors
Constructor and Description
`RegressionByDiscretization()` Default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete 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

Class RegressionByDiscretization

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

RegressionByDiscretization

Method Detail

globalInfo

getCapabilities

buildClassifier

classifyInstance

listOptions

setOptions

getOptions

numBinsTipText

getNumBins

setNumBins

deleteEmptyBinsTipText

getDeleteEmptyBins

setDeleteEmptyBins

useEqualFrequencyTipText

getUseEqualFrequency

setUseEqualFrequency

toString

getRevision

main