Package weka.classifiers.meta

Class Bagging

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class Bagging
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
    Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner.

    For more information, see

    Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

    BibTeX: 

     @article{Breiman1996,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {2},
    pages = {123-140},
    title = {Bagging predictors},
    volume = {24},
    year = {1996}
 }

    Valid options are: 

     -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
 
     -O
  Calculate the out of bag error.
 
     -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.REPTree)
 
     Options specific to classifier weka.classifiers.trees.REPTree:
 
     -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
 
     -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 
     -N <number of folds>
  Number of folds for reduced error pruning (default 3).
 
     -S <seed>
  Seed for random data shuffling (default 1).
 
     -P
  No pruning.
 
     -L
  Maximum tree depth (default -1, no maximum)
    Options after -- are passed to the designated classifier.

    Version:
    $Revision: 11572 $
    Author:
    Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • Bagging

        public Bagging()
        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: 

         -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
 
         -O
  Calculate the out of bag error.
 
         -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.REPTree)
 
         Options specific to classifier weka.classifiers.trees.REPTree:
 
         -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
 
         -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 
         -N <number of folds>
  Number of folds for reduced error pruning (default 3).
 
         -S <seed>
  Seed for random data shuffling (default 1).
 
         -P
  No pruning.
 
         -L
  Maximum tree depth (default -1, no maximum)
        Options after -- are passed to the designated classifier.

        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

      • bagSizePercentTipText

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

      • getBagSizePercent

        public int getBagSizePercent()
        Gets the size of each bag, as a percentage of the training set size.
        Returns:
        the bag size, as a percentage.

      • setBagSizePercent

        public void setBagSizePercent​(int newBagSizePercent)
        Sets the size of each bag, as a percentage of the training set size.
        Parameters:
        newBagSizePercent - the bag size, as a percentage.

      • calcOutOfBagTipText

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

      • setCalcOutOfBag

        public void setCalcOutOfBag​(boolean calcOutOfBag)
        Set whether the out of bag error is calculated.
        Parameters:
        calcOutOfBag - whether to calculate the out of bag error

      • getCalcOutOfBag

        public boolean getCalcOutOfBag()
        Get whether the out of bag error is calculated.
        Returns:
        whether the out of bag error is calculated

      • measureOutOfBagError

        public double measureOutOfBagError()
        Gets the out of bag error that was calculated as the classifier was built.
        Returns:
        the out of bag error

      • enumerateMeasures

        public java.util.Enumeration 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

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Bagging method.
        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

      • 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:
        preedicted class probability distribution
        Throws:
        java.lang.Exception - if distribution can't be computed successfully

      • toString

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

      • main

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