Examples of weka.classifiers.trees.J48

weka.classifiers.trees.j48.PruneableClassifierTree

Class for generating a pruned or unpruned C4.5 decision tree. For more information, see

Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.

BibTeX:

 @book{Quinlan1993, address = {San Mateo, CA}, author = {Ross Quinlan}, publisher = {Morgan Kaufmann Publishers}, title = {C4.5: Programs for Machine Learning}, year = {1993} }

Valid options are:

 -U Use unpruned tree.

 -C <pruning confidence> Set confidence threshold for pruning. (default 0.25)

 -M <minimum number of instances> Set minimum number of instances per leaf. (default 2)

 -R Use reduced error pruning.

 -N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

 -B Use binary splits only.

 -S Don't perform subtree raising.

 -L Do not clean up after the tree has been built.

 -A Laplace smoothing for predicted probabilities.

 -Q <seed> Seed for random data shuffling (default 1).

@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 1.9 $

      modSelection = new C45ModelSelection(m_minNumObj, instances);
    if (!m_reducedErrorPruning)
      m_root = new C45PruneableClassifierTree(modSelection, !m_unpruned, m_CF,
              m_subtreeRaising, !m_noCleanup);
    else
      m_root = new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds,
             !m_noCleanup, m_Seed);
    m_root.buildClassifier(instances);
    if (m_binarySplits) {
      ((BinC45ModelSelection)modSelection).cleanup();
    } else {

View Full Code Here


    try {
      if( !m_reducedErrorPruning ) {
        result = new C45PruneableClassifierTree( null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup ).getCapabilities();
      } else {
        result = new PruneableClassifierTree( null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed ).getCapabilities();
      }
    } catch( Exception e ) {
      result = new Capabilities( this );
    }

View Full Code Here

    ModelSelection modSelection = new HTreeModelSelection( m_minNumObj, instances );


    if( !m_reducedErrorPruning ) {
      m_root = new C45PruneableClassifierTree( modSelection, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup );
    } else {
      m_root = new PruneableClassifierTree( modSelection, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed );
    }
    m_root.buildClassifier( instances );
  }

View Full Code Here


    try {
      if( !m_reducedErrorPruning ) {
        result = new C45PruneableClassifierTree( null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup ).getCapabilities();
      } else {
        result = new PruneableClassifierTree( null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed ).getCapabilities();
      }
    } catch( Exception e ) {
      result = new Capabilities( this );
    }

View Full Code Here

    ModelSelection modSelection = new HDIGModelSelection( m_minNumObj, instances );


    if( !m_reducedErrorPruning ) {
      m_root = new C45PruneableClassifierTree( modSelection, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup );
    } else {
      m_root = new PruneableClassifierTree( modSelection, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed );
    }
    m_root.buildClassifier( instances );
  }

View Full Code Here


    try {
      if( !m_reducedErrorPruning ) {
        result = new C45PruneableClassifierTree( null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup ).getCapabilities();
      } else {
        result = new PruneableClassifierTree( null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed ).getCapabilities();
      }
    } catch( Exception e ) {
      result = new Capabilities( this );
    }

View Full Code Here

    }
    if( !m_reducedErrorPruning ) {
      m_root = new C45PruneableClassifierTree( modSelection, !m_unpruned, m_CF,
          m_subtreeRaising, !m_noCleanup );
    } else {
      m_root = new PruneableClassifierTree( modSelection, !m_unpruned, m_numFolds,
          !m_noCleanup, m_Seed );
    }
    m_root.buildClassifier( instances );
    if( m_binarySplits ) {
      ( (BinC45ModelSelection) modSelection ).cleanup();

View Full Code Here

    
    try {
      if (!m_reducedErrorPruning)
        result = new C45PruneableClassifierTree(null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup, m_collapseTree).getCapabilities();
      else
        result = new PruneableClassifierTree(null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
      result.disableAll();
    }

View Full Code Here

      modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    if (!m_reducedErrorPruning)
      m_root = new C45PruneableClassifierTree(modSelection, !m_unpruned, m_CF,
                                              m_subtreeRaising, !m_noCleanup, m_collapseTree);
    else
      m_root = new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds,
             !m_noCleanup, m_Seed);
    m_root.buildClassifier(instances);
    if (m_binarySplits) {
      ((BinC45ModelSelection)modSelection).cleanup();
    } else {

View Full Code Here

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