weka.classifiers.meta

Class AdditiveRegression

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.meta.AdditiveRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class AdditiveRegression
extends IteratedSingleClassifierEnhancer
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier

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. Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.

For more information see:

J.H. Friedman (1999). Stochastic Gradient Boosting.

BibTeX:

 @techreport{Friedman1999,
    author = {J.H. Friedman},
    institution = {Stanford University},
    title = {Stochastic Gradient Boosting},
    year = {1999},
    PS = {http://www-stat.stanford.edu/\~jhf/ftp/stobst.ps}
 }
 

Valid options are:

 -S
  Specify shrinkage rate. (default = 1.0, ie. no shrinkage)
 

 -I <num>
  Number of iterations.
  (default 10)

 -A
  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
 
 
 -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.DecisionStump)
 
 
 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
 
 
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
 

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
AdditiveRegression() Default constructor specifying DecisionStump as the classifier
AdditiveRegression(Classifier classifier) Constructor which takes base classifier as argument.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Method used to build the classifier.
double	classifyInstance(Instance inst) Classify an instance.
void	done() Clean up.
java.util.Enumeration<java.lang.String>	enumerateMeasures() Returns an enumeration of the additional measure names
Capabilities	getCapabilities() Returns default capabilities of the classifier.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
boolean	getMinimizeAbsoluteError() Gets whether absolute error is to be minimized.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
double	getShrinkage() Get the shrinkage rate.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
java.lang.String	globalInfo() Returns a string describing this attribute evaluator
void	initializeClassifier(Instances data) Initialize classifier.
java.util.Enumeration<Option>	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
java.lang.String	minimizeAbsoluteErrorTipText() Returns the tip text for this property
boolean	next() Perform another iteration.
void	setMinimizeAbsoluteError(boolean f) Sets whether absolute error is to be minimized.
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, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.classifiers.Classifier

distributionForInstance

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

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public java.util.Enumeration<Option> 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:

 -S
  Specify shrinkage rate. (default = 1.0, ie. no shrinkage)
 

 -I <num>
  Number of iterations.
  (default 10)

 -A
  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
  
 
 -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.DecisionStump)
 
 
 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
 
 
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
   

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

minimizeAbsoluteErrorTipText

public java.lang.String minimizeAbsoluteErrorTipText()

Returns the tip text for this property

Returns:

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

setMinimizeAbsoluteError

public void setMinimizeAbsoluteError(boolean f)

Sets whether absolute error is to be minimized.

Parameters:

f - true if absolute error is to be minimized.

getMinimizeAbsoluteError

public boolean getMinimizeAbsoluteError()

Gets whether absolute error is to be minimized.

Returns:

true if absolute error is to be minimized

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface 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 data)
                     throws java.lang.Exception

Method used to build the classifier.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

data - the training data to be used for generating the bagged classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

initializeClassifier

public void initializeClassifier(Instances data)
                          throws java.lang.Exception

Initialize classifier.

Specified by:

initializeClassifier in interface IterativeClassifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if the classifier could not be initialized successfully

next

public boolean next()
             throws java.lang.Exception

Perform another iteration.

Specified by:

next in interface IterativeClassifier

Returns:

false if no further iterations could be performed, true otherwise

Throws:

java.lang.Exception - if this iteration fails for unexpected reasons

done

public void done()

Clean up.

Specified by:

done in interface IterativeClassifier

classifyInstance

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

Classify an instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

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<java.lang.String> 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:

additionalMeasureName - 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

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

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]