Examples of weka.classifiers.trees.J48

weka.classifiers.trees.j48.Distribution

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 $

  public Capabilities getCapabilities() {
    Capabilities      result;
    
    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);

View Full Code Here

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    else
      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);

View Full Code Here

   */
  public Capabilities getCapabilities() {
    Capabilities      result;


    try {
     result = new C45PruneableClassifierTreeG(null, !m_unpruned, m_CF, m_subtreeRaising, m_relabel, !m_noCleanup).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
      result.disableAll();
    }

View Full Code Here


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
      m_root = new C45PruneableClassifierTreeG(modSelection, 
                              !m_unpruned, m_CF, m_subtreeRaising, 
                               m_relabel, !m_noCleanup);
    m_root.buildClassifier(instances);


    if (m_binarySplits) {

View Full Code Here

   */
  public Capabilities getCapabilities() {
    Capabilities      result;


    try {
     result = new C45PruneableClassifierTreeG(null, !m_unpruned, m_CF, m_subtreeRaising, m_relabel, !m_noCleanup).getCapabilities();
    }
    catch (Exception e) {
      result = new Capabilities(this);
    }

View Full Code Here


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
      m_root = new C45PruneableClassifierTreeG(modSelection, 
                              !m_unpruned, m_CF, m_subtreeRaising, 
                               m_relabel, !m_noCleanup);
    m_root.buildClassifier(instances);


    if (m_binarySplits) {

View Full Code Here

    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = data.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)data));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(data);
    if (m_localModel.numSubsets() > 1) {

View Full Code Here

    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = train.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)train));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(train, test);
    m_test = new Distribution(test, m_localModel);
    if (m_localModel.numSubsets() > 1) {
      localTrain = m_localModel.split(train);
      localTest = m_localModel.split(test);
      train = null;
      test = null;

View Full Code Here

  /**
   * Computes error estimate for tree.
   */
  private double errorsForTree() throws Exception {


    Distribution test;


    if (m_isLeaf)
      return errorsForLeaf();
    else {
      double error = 0;

View Full Code Here

    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = data.sumOfWeights();
    noSplit = new NoSplit (new Distribution((Instances)data));
    if (leaf)
      m_localModel = noSplit;
    else
      m_localModel = m_toSelectModel.selectModel(data);
    if (m_localModel.numSubsets() > 1) {

View Full Code Here

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