Examples of weka.classifiers.trees.J48

weka.classifiers.trees.j48.ModelSelection

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 $

    } else {
      getSplitPoint();
      m_numSubsets = 2;
    }
    //create distribution for data
    m_distribution = new Distribution(data, this);  
  }

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      double[][] dataZs, double[][] dataWs) throws Exception{


    int numAttributes = data.numAttributes();


    if (numAttributes < 2) throw new Exception("Can't select Model without non-class attribute");
    if (data.numInstances() < m_minNumInstances) return new NoSplit(new Distribution(data));




    double bestGain = -Double.MAX_VALUE;
    int bestAttribute = -1;


    //try split on every attribute
    for (int i = 0; i < numAttributes; i++) {
      if (i != data.classIndex()) {


  //build split
  ResidualSplit split = new ResidualSplit(i);      
  split.buildClassifier(data, dataZs, dataWs);


  if (split.checkModel(m_minNumInstances)){


    //evaluate split 
    double gain = split.entropyGain();  
    if (gain > bestGain) {
      bestGain = gain;
      bestAttribute = i;
    }
  }
      }          
    }     


    if (bestGain >= m_minInfoGain){
      //return best split
      ResidualSplit split = new ResidualSplit(bestAttribute);
      split.buildClassifier(data, dataZs, dataWs);  
      return split;      
    } else {      
      //could not find any split with enough information gain
      return new NoSplit(new Distribution(data));      
    }
  }

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    Instances [] localInstances;
    double errors = 0;
    int i;


    if (m_isLeaf)
      return getEstimatedErrorsForDistribution(new Distribution(data));
    else{
      Distribution savedDist = m_localModel.distribution();
      m_localModel.resetDistribution(data);
      localInstances = (Instances[])m_localModel.split(data);
      //m_localModel.m_distribution=savedDist;
      for (i=0;i<m_sons.length;i++)
        errors = errors+

View Full Code Here

   *
   * @param instances the data to train the classifier with
   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier( Instances instances ) throws Exception {
    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 );

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   *
   * @param instances the data to train the classifier with
   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier( Instances instances ) throws Exception {
    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 );

View Full Code Here

   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier(Instances instances) 
       throws Exception {


    ModelSelection modSelection;   


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);

View Full Code Here

   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier(Instances instances)
       throws Exception {


    ModelSelection modSelection;


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);

View Full Code Here

    }


    int minNumInstances = 2;
  
    //create ModelSelection object, either for splits on the residuals or for splits on the class value 
    ModelSelection modSelection;  
    if (m_splitOnResiduals) {
      modSelection = new ResidualModelSelection(minNumInstances);
    } else {
      modSelection = new C45ModelSelection(minNumInstances, filteredData);
    }

View Full Code Here

    }


    int minNumInstances = 2;
  
    //create ModelSelection object, either for splits on the residuals or for splits on the class value 
    ModelSelection modSelection;  
    if (m_splitOnResiduals) {
      modSelection = new ResidualModelSelection(minNumInstances);
    } else {
      modSelection = new C45ModelSelection(minNumInstances, filteredData);
    }

View Full Code Here

   *
   * @param instances the data to train the classifier with
   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier( Instances instances ) throws Exception {
    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 );

View Full Code Here

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