Package weka.classifiers.meta

Class 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
    Version:
    $Revision: 15022 $
    Author:
    Mark Hall (mhall@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • 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

      • 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

      • 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

      • resumeTipText

        public java.lang.String resumeTipText()
        Tool tip text for the resume property
        Returns:
        the tool tip text for the finalize property

      • setResume

        public void setResume​(boolean resume)
        If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
        Specified by:
        setResume in interface IterativeClassifier
        Parameters:
        resume - true if the model is to be finalized after performing iterations

      • getResume

        public boolean getResume()
        Returns true if the model is to be finalized (or has been finalized) after training.
        Specified by:
        getResume in interface IterativeClassifier
        Returns:
        the current value of finalize

      • 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

      • 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

      • 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]