Package weka.classifiers.meta

Class LogitBoost

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class LogitBoost
extends RandomizableIteratedSingleClassifierEnhancer
implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler
    Class for performing additive logistic regression.
    This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

    J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

    Can do efficient internal cross-validation to determine appropriate number of iterations.

    BibTeX: 

     @techreport{Friedman1998,
    address = {Stanford University},
    author = {J. Friedman and T. Hastie and R. Tibshirani},
    title = {Additive Logistic Regression: a Statistical View of Boosting},
    year = {1998},
    PS = {http://www-stat.stanford.edu/\~jhf/ftp/boost.ps}
 }

    Valid options are: 

     -Q
  Use resampling instead of reweighting for boosting.
     -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
     -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)
     -R <num>
  Number of runs for internal cross-validation.
  (default 1)
     -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)
     -H <num>
  Shrinkage parameter.
  (default 1)
     -S <num>
  Random number seed.
  (default 1)
     -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
    Options after -- are passed to the designated learner.

    Version:
    $Revision: 9371 $
    Author:
    Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • LogitBoost

        public LogitBoost()
        Constructor.

    • Method Detail

      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing classifier
        Returns:
        a description 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: 

         -Q
  Use resampling instead of reweighting for boosting.
         -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
         -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)
         -R <num>
  Number of runs for internal cross-validation.
  (default 1)
         -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)
         -H <num>
  Shrinkage parameter.
  (default 1)
         -S <num>
  Random number seed.
  (default 1)
         -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
        Options after -- are passed to the designated learner.

        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class RandomizableIteratedSingleClassifierEnhancer
        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

      • getShrinkage

        public double getShrinkage()
        Get the value of Shrinkage.
        Returns:
        Value of Shrinkage.

      • setShrinkage

        public void setShrinkage​(double newShrinkage)
        Set the value of Shrinkage.
        Parameters:
        newShrinkage - Value to assign to Shrinkage.

      • likelihoodThresholdTipText

        public java.lang.String likelihoodThresholdTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • getLikelihoodThreshold

        public double getLikelihoodThreshold()
        Get the value of Precision.
        Returns:
        Value of Precision.

      • setLikelihoodThreshold

        public void setLikelihoodThreshold​(double newPrecision)
        Set the value of Precision.
        Parameters:
        newPrecision - Value to assign to Precision.

      • numRunsTipText

        public java.lang.String numRunsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • getNumRuns

        public int getNumRuns()
        Get the value of NumRuns.
        Returns:
        Value of NumRuns.

      • setNumRuns

        public void setNumRuns​(int newNumRuns)
        Set the value of NumRuns.
        Parameters:
        newNumRuns - Value to assign to NumRuns.

      • numFoldsTipText

        public java.lang.String numFoldsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • getNumFolds

        public int getNumFolds()
        Get the value of NumFolds.
        Returns:
        Value of NumFolds.

      • setNumFolds

        public void setNumFolds​(int newNumFolds)
        Set the value of NumFolds.
        Parameters:
        newNumFolds - Value to assign to NumFolds.

      • useResamplingTipText

        public java.lang.String useResamplingTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setUseResampling

        public void setUseResampling​(boolean r)
        Set resampling mode
        Parameters:
        r - true if resampling should be done

      • getUseResampling

        public boolean getUseResampling()
        Get whether resampling is turned on
        Returns:
        true if resampling output is on

      • weightThresholdTipText

        public java.lang.String weightThresholdTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setWeightThreshold

        public void setWeightThreshold​(int threshold)
        Set weight thresholding
        Parameters:
        threshold - the percentage of weight mass used for training

      • getWeightThreshold

        public int getWeightThreshold()
        Get the degree of weight thresholding
        Returns:
        the percentage of weight mass used for training

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Builds the boosted classifier
        Overrides:
        buildClassifier in class IteratedSingleClassifierEnhancer
        Parameters:
        data - the data to train the classifier with
        Throws:
        java.lang.Exception - if building fails, e.g., can't handle data

      • classifiers

        public Classifier[][] classifiers()
        Returns the array of classifiers that have been built.
        Returns:
        the built classifiers

      • distributionForInstance

        public double[] distributionForInstance​(Instance instance)
                                 throws java.lang.Exception
        Calculates the class membership probabilities for the given test instance.
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        instance - the instance to be classified
        Returns:
        predicted class probability distribution
        Throws:
        java.lang.Exception - if instance could not be classified successfully

      • toSource

        public java.lang.String toSource​(java.lang.String className)
                          throws java.lang.Exception
        Returns the boosted model as Java source code.
        Specified by:
        toSource in interface Sourcable
        Parameters:
        className - the classname in the generated code
        Returns:
        the tree as Java source code
        Throws:
        java.lang.Exception - if something goes wrong

      • toString

        public java.lang.String toString()
        Returns description of the boosted classifier.
        Overrides:
        toString in class java.lang.Object
        Returns:
        description of the boosted classifier as a string

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - the options