Examples of weka.classifiers.trees.J48

weka.classifiers.trees.j48.C45ModelSelection

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 $

    ModelSelection modSelection;


    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);

View Full Code Here

    //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);
    }
  
    //create tree root
    m_tree = new LMTNode(modSelection, m_numBoostingIterations, m_fastRegression, 
       m_errorOnProbabilities, m_minNumInstances, m_weightTrimBeta, m_useAIC);

View Full Code Here

    //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);
    }
  
    //create tree root
    m_tree = new LMTNode(modSelection, m_numBoostingIterations, m_fastRegression, 
       m_errorOnProbabilities, m_minNumInstances, m_weightTrimBeta, m_useAIC);

View Full Code Here

    ModelSelection modSelection;


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

View Full Code Here

        // Computes constructor error
        if (data.instance(j).classValue()!=getConstError(probsConst)) m_constError=m_constError +1;
      }
        
    //to choose split point on the node data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
    m_localModel = m_modelSelection.selectModel(data);
       
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {
      grow = (m_localModel.numSubsets() > 1);

View Full Code Here

          data.instance(j).setValue(i,probsConst[i]);
      } 
  
        
    //needed by dynamic data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
      
    m_localModel = m_modelSelection.selectModel(data);
       
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {

View Full Code Here

        for (int i = 0; i<data.classAttribute().numValues() ; i++)
          data.instance(j).setValue(i,probsConst[i]);
      }
        
    // needed by dynamic data
    m_modelSelection=new  C45ModelSelection(m_minNumInstances, data);
    m_localModel = m_modelSelection.selectModel(data);
    //split node if more than minNumInstances...
    if (m_numInstances > m_minNumInstances) {
      grow = (m_localModel.numSubsets() > 1);
    } else {

View Full Code Here

    ModelSelection modSelection;   


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
    if (m_unpruned) 
      m_root = new MakeDecList(modSelection, m_minNumObj);
    else if (m_reducedErrorPruning) 
      m_root = new MakeDecList(modSelection, m_numFolds, m_minNumObj, m_Seed);
    else

View Full Code Here

    ModelSelection modSelection;


    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);

View Full Code Here

    ModelSelection modSelection;   


    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
    if (m_unpruned) 
      m_root = new MakeDecList(modSelection, m_minNumObj);
    else if (m_reducedErrorPruning) 
      m_root = new MakeDecList(modSelection, m_numFolds, m_minNumObj, m_Seed);
    else

View Full Code Here

0 1 2 3 4 5 6 7 8

TOP

Related Classes of weka.classifiers.trees.J48

de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Evaluator

edu.brown.markov.FeatureClusterer

edu.uga.cs.fluxbuster.classification.Classifier

etc.aloe.cscw2013.WekaModelTest

eu.admire.ogsadai.activity.weka.WekaUtilitiesTest

trust.weka4jason.WekaBinClassifier

trust.weka4jason.WekaClusterClassifier

weka.classifiers.bayes.NaiveBayesUpdateable

weka.classifiers.Evaluation

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.