Package weka.classifiers.meta

Class 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 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

      • 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

      • 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.

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - the options