Examples of weka.classifiers.meta.Bagging

weka.classifiers.meta.Bagging

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.

@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (len@reeltwo.com) @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) @version $Revision: 6502 $


    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();
    
    m_bagger = new Bagging();
    RandomTree rTree = new RandomTree();


    // set up the random tree options
    m_KValue = m_numFeatures;
    if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes())+1;

View Full Code Here

       * weka.classifiers.trees.J48 -- -C 0.25 -M 2");
       * classifier.setOptions(options);
       */
    } else {


      classifier = new Bagging();
      // try also
      // classifier.setOptions(Utils.splitOptions("-P 10 -S 1 -I 10 -W
      // weka.classifiers.trees.J48 -- -U -M 2")) ;
      String optionsString = "-P 100 -S 1 -I 10 -W weka.classifiers.trees.M5P -- -U -M 7.0";
      String[] options = Utils.splitOptions(optionsString);

View Full Code Here


    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();
    
    m_bagger = new Bagging();
    RandomTree rTree = new RandomTree();


    // set up the random tree options
    m_KValue = m_numFeatures;
    if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes())+1;

View Full Code Here


    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();
    
    m_bagger = new Bagging();
    RandomTree rTree = new RandomTree();


    // set up the random tree options
    m_KValue = m_numFeatures;
    if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes())+1;

View Full Code Here

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Related Classes of weka.classifiers.meta.Bagging

maui.filters.MauiFilter

weka.classifiers.trees.RandomForest

weka.core.Instances

weka.core.Option

weka.core.TechnicalInformation

java.util.Enumeration

java.util.Random

java.util.Vector

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