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Class weka.classifiers.RegressionByDiscretization 

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.RegressionByDiscretization
public class RegressionByDiscretization
extends Classifier
implements OptionHandler
Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute 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.

-W classname
Specify the full class name of a classifier as the basis for regression (required).

-B num
The number of bins the class attribute will be discretized into. (default 10)

-O
Optimize number of bins (values up to and including the -B option will be considered). (default no debugging output)

Any options after -- will be passed to the sub-classifier.

Version:
$Revision: 1.13 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)

Constructor Index

o RegressionByDiscretization()
 

Method Index

o buildClassifier(Instances)
Generates the classifier.
o classifyInstance(Instance)
Returns a predicted class for the test instance.
o getClassifier()
Get the classifier used as the classifier
o getDebug()
Gets whether debugging output will be printed
o getNumBins()
Gets the number of bins the class attribute will be discretized into.
o getOptimizeBins()
Gets whether the discretizer optimizes the number of bins
o getOptions()
Gets the current settings of the Classifier.
o listOptions()
Returns an enumeration describing the available options
o main(String[])
Main method for testing this class.
o setClassifier(Classifier)
Set the classifier for boosting.
o setDebug(boolean)
Sets whether debugging output will be printed
o setNumBins(int)
Sets the number of bins the class attribute will be discretized into.
o setOptimizeBins(boolean)
Sets whether the discretizer optimizes the number of bins
o setOptions(String[])
Parses a given list of options.
o toString()
Returns a description of the classifier.

Constructor Detail

o RegressionByDiscretization
public RegressionByDiscretization()

Method Detail

o buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
          Generates the classifier.
Parameters:
instances - set of instances serving as training data
Throws:
java.lang.Exception - if the classifier has not been generated successfully
Overrides:
buildClassifier in class Classifier
o classifyInstance
public double classifyInstance(Instance instance) throws java.lang.Exception
          Returns a predicted class for the test instance.
Parameters:
instance - the instance to be classified
Returns:
predicted class value
Throws:
java.lang.Exception - if the prediction couldn't be made
Overrides:
classifyInstance in class Classifier
o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options
Returns:
an enumeration of all the available options
o 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 classname
Specify the full class name of a classifier as the basis for regression (required).

-B num
The number of bins the class attribute will be discretized into. (default 10)

-O
Optimize number of bins (values up to and including the -B option will be considered). (default no debugging output)

Any options after -- will be passed to the sub-classifier.

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
Returns:
an array of strings suitable for passing to setOptions
o setClassifier
public void setClassifier(Classifier newClassifier)
          Set the classifier for boosting.
Parameters:
newClassifier - the Classifier to use.
o getClassifier
public Classifier getClassifier()
          Get the classifier used as the classifier
Returns:
the classifier used as the classifier
o setOptimizeBins
public void setOptimizeBins(boolean optimize)
          Sets whether the discretizer optimizes the number of bins
Parameters:
optimize - true if the discretizer should optimize the number of bins
o getOptimizeBins
public boolean getOptimizeBins()
          Gets whether the discretizer optimizes the number of bins
Returns:
true if the discretizer should optimize the number of bins
o setDebug
public void setDebug(boolean debug)
          Sets whether debugging output will be printed
Parameters:
debug - true if debug output should be printed
o getDebug
public boolean getDebug()
          Gets whether debugging output will be printed
Returns:
true if debug output should be printed
o setNumBins
public void setNumBins(int numBins)
          Sets the number of bins the class attribute will be discretized into.
Parameters:
numBins - the number of bins to use
o getNumBins
public int getNumBins()
          Gets the number of bins the class attribute will be discretized into.
Returns:
the number of bins to use
o toString
public java.lang.String toString()
          Returns a description of the classifier.
Returns:
a description of the classifier as a string.
Overrides:
toString in class java.lang.Object
o main
public static void main(java.lang.String argv[])
          Main method for testing this class.
Parameters:
argv - the options 
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