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Class weka.classifiers.RegressionByDiscretization 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.RegressionByDiscretization
public class RegressionByDiscretization
extends Classifier
implements OptionHandler, WeightedInstancesHandler
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: 1.5 $
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.

Constructors

o RegressionByDiscretization 
 public RegressionByDiscretization()

Methods

o buildClassifier 
 public void buildClassifier(Instances instances) throws Exception
Generates the classifier.

Parameters:
instances - set of instances serving as training data
Throws: Exception
if the classifier has not been generated successfully
Overrides:
buildClassifier in class Classifier
o classifyInstance 
 public double classifyInstance(Instance instance) throws Exception
Returns a predicted class for the test instance.

Parameters:
instance - the instance to be classified
Returns:
predicted class value
Throws: Exception
if the prediction couldn't be made
Overrides:
classifyInstance in class Classifier
o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
o setOptions 
 public void setOptions(String options[]) throws 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: Exception
if an option is not supported
o getOptions 
 public 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 String toString()
Returns a description of the classifier.

Returns:
a description of the classifier as a string.
Overrides:
toString in class Object
o main 
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - the options 
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