public class AdditiveRegression extends IteratedSingleClassifierEnhancer implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier
@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
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT| Constructor and Description |
|---|
AdditiveRegression()
Default constructor specifying DecisionStump as the classifier
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AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.
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| Modifier and Type | Method and Description |
|---|---|
void |
buildClassifier(Instances data)
Method used to build the classifier.
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double |
classifyInstance(Instance inst)
Classify an instance.
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void |
done()
Clean up.
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java.util.Enumeration<java.lang.String> |
enumerateMeasures()
Returns an enumeration of the additional measure names
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure
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boolean |
getMinimizeAbsoluteError()
Gets whether absolute error is to be minimized.
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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double |
getShrinkage()
Get the shrinkage rate.
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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.
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java.lang.String |
globalInfo()
Returns a string describing this attribute evaluator
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void |
initializeClassifier(Instances data)
Initialize classifier.
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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double |
measureNumIterations()
return the number of iterations (base classifiers) completed
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java.lang.String |
minimizeAbsoluteErrorTipText()
Returns the tip text for this property
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boolean |
next()
Perform another iteration.
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void |
setMinimizeAbsoluteError(boolean f)
Sets whether absolute error is to be minimized.
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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void |
setShrinkage(double l)
Set the shrinkage parameter
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java.lang.String |
shrinkageTipText()
Returns the tip text for this property
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java.lang.String |
toString()
Returns textual description of the classifier.
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getNumIterations, numIterationsTipText, setNumIterationsclassifierTipText, getClassifier, postExecution, preExecution, setClassifierbatchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacesequals, getClass, hashCode, notify, notifyAll, wait, wait, waitdistributionForInstancepublic AdditiveRegression()
public AdditiveRegression(Classifier classifier)
classifier - the base classifier to usepublic java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.util.Enumeration<Option> listOptions()
listOptions in interface OptionHandlerlistOptions in class IteratedSingleClassifierEnhancerpublic void setOptions(java.lang.String[] options)
throws java.lang.Exception
-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
setOptions in interface OptionHandlersetOptions in class IteratedSingleClassifierEnhanceroptions - the list of options as an array of stringsjava.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class IteratedSingleClassifierEnhancerpublic java.lang.String shrinkageTipText()
public void setShrinkage(double l)
l - the shrinkage rate.public double getShrinkage()
public java.lang.String minimizeAbsoluteErrorTipText()
public void setMinimizeAbsoluteError(boolean f)
f - true if absolute error is to be minimized.public boolean getMinimizeAbsoluteError()
public Capabilities getCapabilities()
getCapabilities in interface ClassifiergetCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilitiespublic void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier in interface ClassifierbuildClassifier in class IteratedSingleClassifierEnhancerdata - the training data to be used for generating the
bagged classifier.java.lang.Exception - if the classifier could not be built successfullypublic void initializeClassifier(Instances data) throws java.lang.Exception
initializeClassifier in interface IterativeClassifierdata - the training datajava.lang.Exception - if the classifier could not be initialized successfullypublic boolean next()
throws java.lang.Exception
next in interface IterativeClassifierjava.lang.Exception - if this iteration fails for unexpected reasonspublic void done()
done in interface IterativeClassifierpublic double classifyInstance(Instance inst) throws java.lang.Exception
classifyInstance in interface ClassifierclassifyInstance in class AbstractClassifierinst - the instance to predictjava.lang.Exception - if an error occurspublic java.util.Enumeration<java.lang.String> enumerateMeasures()
enumerateMeasures in interface AdditionalMeasureProducerpublic double getMeasure(java.lang.String additionalMeasureName)
getMeasure in interface AdditionalMeasureProduceradditionalMeasureName - the name of the measure to query for its valuejava.lang.IllegalArgumentException - if the named measure is not supportedpublic double measureNumIterations()
public java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class AbstractClassifierpublic static void main(java.lang.String[] argv)
argv - should contain the following arguments:
-t training file [-T test file] [-c class index]