Package weka.classifiers.meta
Class AdditiveRegression
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.meta.AdditiveRegression
- All Implemented Interfaces:
Serializable
,Cloneable
,AdditionalMeasureProducer
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class AdditiveRegression
extends IteratedSingleClassifierEnhancer
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler
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:
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)
-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
- Version:
- $Revision: 1.25 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
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Constructor Summary
ConstructorsConstructorDescriptionDefault constructor specifying DecisionStump as the classifierAdditiveRegression
(Classifier classifier) Constructor which takes base classifier as argument. -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances data) Build the classifier on the supplied datadouble
classifyInstance
(Instance inst) Classify an instance.Returns an enumeration of the additional measure namesReturns default capabilities of the classifier.double
getMeasure
(String additionalMeasureName) Returns the value of the named measureString[]
Gets the current settings of the Classifier.Returns the revision string.double
Get the shrinkage rate.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.Returns a string describing this attribute evaluatorReturns an enumeration describing the available options.static void
Main method for testing this class.double
return the number of iterations (base classifiers) completedvoid
setOptions
(String[] options) Parses a given list of options.void
setShrinkage
(double l) Set the shrinkage parameterReturns the tip text for this propertytoString()
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
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Constructor Details
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AdditiveRegression
public AdditiveRegression()Default constructor specifying DecisionStump as the classifier -
AdditiveRegression
Constructor which takes base classifier as argument.- Parameters:
classifier
- the base classifier to use
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Method Details
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globalInfo
Returns a string describing this attribute evaluator- Returns:
- a description of the evaluator suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
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)
-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 interfaceOptionHandler
- Overrides:
setOptions
in classIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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shrinkageTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setShrinkage
public void setShrinkage(double l) Set the shrinkage parameter- Parameters:
l
- the shrinkage rate.
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getShrinkage
public double getShrinkage()Get the shrinkage rate.- Returns:
- the value of the learning rate
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Build the classifier on the supplied data- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the training data- Throws:
Exception
- if the classifier could not be built successfully
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classifyInstance
Classify an instance.- Overrides:
classifyInstance
in classClassifier
- Parameters:
inst
- the instance to predict- Returns:
- a prediction for the instance
- Throws:
Exception
- if an error occurs
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enumerateMeasures
Returns an enumeration of the additional measure names- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Returns:
- an enumeration of the measure names
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getMeasure
Returns the value of the named measure- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- Parameters:
additionalMeasureName
- the name of the measure to query for its value- Returns:
- the value of the named measure
- Throws:
IllegalArgumentException
- if the named measure is not supported
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measureNumIterations
public double measureNumIterations()return the number of iterations (base classifiers) completed- Returns:
- the number of iterations (same as number of base classifier models)
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toString
Returns textual description of the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- should contain the following arguments: -t training file [-T test file] [-c class index]
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