weka.classifiers.meta
Class AdditiveRegression

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.meta.AdditiveRegression
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, OptionHandler, WeightedInstancesHandler

public class AdditiveRegression
extends IteratedSingleClassifierEnhancer
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler

Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the previous iteration. Prediction is accomplished by adding the predictions of each classifier. Smoothing is accomplished through varying the shrinkage (learning rate) parameter.

For more information see:

Friedman, J.H. (1999). Stochastic Gradient Boosting. Technical Report Stanford University. http://www-stat.stanford.edu/~jhf/ftp/stobst.ps.

Valid options from the command line are:

-W classifierstring
Classifierstring should contain the full class name of a classifier.

-S shrinkage rate
Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). (default = 1.0, ie no shrinkage).

-I max models
Set the maximum number of models to generate. (default = 10).

-D
Debugging output.

Version:
$Revision: 1.17 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
AdditiveRegression()
          Default constructor specifying DecisionStump as the classifier
AdditiveRegression(Classifier classifier)
          Constructor which takes base classifier as argument.
 
Method Summary
 void buildClassifier(Instances data)
          Build the classifier on the supplied data
 double classifyInstance(Instance inst)
          Classify an instance.
 java.util.Enumeration enumerateMeasures()
          Returns an enumeration of the additional measure names
 double getMeasure(java.lang.String additionalMeasureName)
          Returns the value of the named measure
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 double getShrinkage()
          Get the shrinkage rate.
 java.lang.String globalInfo()
          Returns a string describing this attribute evaluator
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 double measureNumIterations()
          return the number of iterations (base classifiers) completed
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setShrinkage(double l)
          Set the shrinkage parameter
 java.lang.String shrinkageTipText()
          Returns the tip text for this property
 java.lang.String toString()
          Returns textual description of the classifier.
 
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
 
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

AdditiveRegression

public AdditiveRegression()
Default constructor specifying DecisionStump as the classifier


AdditiveRegression

public AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.

Parameters:
classifier - the base classifier to use
Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing this attribute evaluator

Returns:
a description of the evaluator suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class IteratedSingleClassifierEnhancer
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:

-W classifierstring
Classifierstring should contain the full class name of a classifier.

-S shrinkage rate
Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). (default = 1.0, ie. no shrinkage).

-D
Debugging output.

-I max models
Set the maximum number of models to generate.

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class IteratedSingleClassifierEnhancer
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 IteratedSingleClassifierEnhancer
Returns:
an array of strings suitable for passing to setOptions

shrinkageTipText

public java.lang.String shrinkageTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setShrinkage

public void setShrinkage(double l)
Set the shrinkage parameter

Parameters:
l - the shrinkage rate.

getShrinkage

public double getShrinkage()
Get the shrinkage rate.

Returns:
the value of the learning rate

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception
Build the classifier on the supplied data

Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the training data
Throws:
java.lang.Exception - if the classifier could not be built successfully

classifyInstance

public double classifyInstance(Instance inst)
                        throws java.lang.Exception
Classify an instance.

Overrides:
classifyInstance in class Classifier
Parameters:
inst - the instance to predict
Returns:
a prediction for the instance
Throws:
java.lang.Exception - if an error occurs

enumerateMeasures

public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names

Specified by:
enumerateMeasures in interface AdditionalMeasureProducer
Returns:
an enumeration of the measure names

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure

Specified by:
getMeasure in interface AdditionalMeasureProducer
Parameters:
measureName - the name of the measure to query for its value
Returns:
the value of the named measure
Throws:
java.lang.IllegalArgumentException - if the named measure is not supported

measureNumIterations

public double measureNumIterations()
return the number of iterations (base classifiers) completed

Returns:
the number of iterations (same as number of base classifier models)

toString

public java.lang.String toString()
Returns textual 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 - should contain the following arguments: -t training file [-T test file] [-c class index]