prtClassBagging  Bagging (Bootstrap Aggregating) classifier
 
     CLASSIFIER = prtClassBagging returns a bagging classifier
 
     CLASSIFIER = prtClassBagging(PROPERTY1, VALUE1, ...) constructs a
     prtClassBagging object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
 
     A prtClassBagging object inherits all properties from the abstract
     class prtClass. In addition is has the following properties:
 
     baseClassifier  - The base classifier to be used 
     nBags           - The number of bags to aggregate over
     nSamplesPerBag  - The number of bootstrap samples to use per bag.
            When nSamplesPerBag is an empty matrix (the default),
            the number of bootstrap samples is set to the number of
            observations in the training data set.
     bootstrapByClass - A logical describing whether to enforce an
            equal number of bootstrap samples from each class in the
            training data set. If bootstrapByClass is true,
            floor(nSamplesPerBag/nClasses) samples per class are used
            when training each classifier.  bootstrapByClass defaults
            to false.
  
     Bagging classifiers are meta-classifiers that attempt to develop
     more robust decision boundaries by aggregating outputs over
     multiple bootstrapped samples of the original data.  For more
     information on bagging classifiers, see:
 
     http://en.wikipedia.org/wiki/Bootstrap_aggregating
 
     A prtClassBagging  object inherits the TRAIN, RUN, 
     CROSSVALIDATE and KFOLDS methods from prtAction. It also inherits 
     the PLOT method from prtClass.
 
     Example:
 
      TestDataSet = prtDataGenUnimodal;       % Create some test and
      TrainingDataSet = prtDataGenUniModal;   % training data
      classifier = prtClassBagging;           % Create a classifier
      classifier.baseClassifier = prtClassMap; % Set the classifier to
                                               % a prtClassMap
      classifier = classifier.train(TrainingDataSet);    % Train
      classified = run(classifier, TestDataSet);         % Test
      subplot(2,1,1);
      classifier.plot;
      subplot(2,1,2);
      [pf,pd] = prtScoreRoc(classified,TestDataSet);
      h = plot(pf,pd,'linewidth',3);
      title('ROC'); xlabel('Pf'); ylabel('Pd');