Examples of weka.classifiers.trees.REPTree

weka.classifiers.trees.REPTree

Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).

Valid options are:

 -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)

@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 6952 $

      System.out
      .println("Or edit the list directly with the LibraryEditor from the GUI");
      
      for (int i = 0; i < 10; i++) {
  
  REPTree tree = new REPTree();
  tree.setSeed(i);
  m_library.addModel(new EnsembleSelectionLibraryModel(tree));
  
      }
      
    }

Examples of weka.classifiers.trees.REPTree

Related Classes of weka.classifiers.trees.REPTree