RegressionByDiscretization

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## weka.classifiers.meta
Class RegressionByDiscretization

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              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

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