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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.meta.AdditiveRegression
public class AdditiveRegression
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.
| Constructor Summary | |
|---|---|
AdditiveRegression()
Default constructor specifying DecisionStump as the classifier |
|
AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument. |
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| 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 |
|---|
public AdditiveRegression()
public AdditiveRegression(Classifier classifier)
classifier - the base classifier to use| Method Detail |
|---|
public java.lang.String globalInfo()
public java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class IteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-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.
setOptions in interface OptionHandlersetOptions in class IteratedSingleClassifierEnhanceroptions - the list of options as an array of strings
java.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 void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class IteratedSingleClassifierEnhancerdata - the training data
java.lang.Exception - if the classifier could not be built successfully
public double classifyInstance(Instance inst)
throws java.lang.Exception
classifyInstance in class Classifierinst - the instance to predict
java.lang.Exception - if an error occurspublic java.util.Enumeration enumerateMeasures()
enumerateMeasures in interface AdditionalMeasureProducerpublic double getMeasure(java.lang.String additionalMeasureName)
getMeasure in interface AdditionalMeasureProducermeasureName - the name of the measure to query for its value
java.lang.IllegalArgumentException - if the named measure is not supportedpublic double measureNumIterations()
public java.lang.String toString()
toString in class java.lang.Objectpublic static void main(java.lang.String[] argv)
argv - should contain the following arguments:
-t training file [-T test file] [-c class index]
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