prtClassTreeBaggingCap  Tree bagging central axis projection classifier
 
     CLASSIFIER = prtClassTreeBaggingCap Tree bagging central axis
     projection classifier.  This classifier is based on the "Random
     Forest" classifier described in 
 
     Breiman, Leo (2001). "Random Forests". Machine Learning 45
 
     CLASSIFIER = prtClassTreeBaggingCap(PROPERTY1, VALUE1, ...) constructs a
     prtClassTreeBaggingCap object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
 
     A prtClassTreeBaggingCap object inherits all properties from the abstract class
     prtClass. In addition is has the following properties:
 
     nTrees                       - The number of trees
     nFeatures                    - The number of features
 
     featureSelectWithReplacement - Flag indicating whether or not to
                                    do feature selection with 
                                    replacement
 
     bootStrapDataAtRoots         - Flag indicating whether or not
                                    to bootstrap at roots
 
     useMex                       - Flag indicating wheter or not to
                                    use the Mex file for speedup.
 
     fastTraining                 - Flag indicating whether to use
                                    "fast" training.  Fast training
                                    does not necessarily choose the
                                    optimal operating point at each
                                    node, but is much faster, and often
                                    has competetive (or even superior)
                                    cross-validation performance, at
                                    the expense of increased
                                    tree-length.
 
     computePercIncrMisclassRate  - Flag indicating whether or not to
                                    compute percent increase in
                                    misclassification rate
     
 
   For more information on random tree classifiers, see:
    http://en.wikipedia.org/wiki/Random_forest
    http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
 
     A prtClassTreeBaggingCap  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 = classifier.train(TrainingDataSet);    % Train
      classified = run(classifier, TestDataSet);         % Test
      classifier.plot;