Package weka.classifiers.meta

Class RandomCommittee

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.ParallelIteratedSingleClassifierEnhancer
weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
weka.classifiers.meta.RandomCommittee
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedInstancesHandler
public class RandomCommittee extends RandomizableParallelIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, PartitionGenerator
Class for building an ensemble of randomizable base classifiers. Each base classifiers is built using a different random number seed (but based one the same data). The final prediction is a straight average of the predictions generated by the individual base classifiers.

Valid options are: 

 -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.RandomTree)
 
 Options specific to classifier weka.classifiers.trees.RandomTree:
 
 -K <number of attributes>
  Number of attributes to randomly investigate
  (<1 = int(log(#attributes)+1)).
 -M <minimum number of instances>
  Set minimum number of instances per leaf.
 -S <num>
  Seed for random number generator.
  (default 1)
 -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
Options after -- are passed to the designated classifier.

Version:
$Revision: 15800 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)
See Also: