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 $

     */
    @Test
    public void testLoad() throws Exception {
        System.out.println("load");


        J48 serializedClassifier = new J48();
        serializedClassifier.setNumFolds(456);
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        ObjectOutputStream serializer = new ObjectOutputStream(out);
        serializer.writeObject(serializedClassifier);
        out.close();
        byte[] serializedStr = out.toByteArray();


        ByteArrayInputStream in = new ByteArrayInputStream(serializedStr);
        WekaModel model = new WekaModel();
        assertTrue(model.load(in));
        in.close();


        Classifier classifier = model.getClassifier();
        assertTrue(classifier instanceof J48);
        J48 j48 = (J48) classifier;
        assertEquals(serializedClassifier.getNumFolds(), j48.getNumFolds());
    }

View Full Code Here

     */
    @Test
    public void testGetPredictedLabels() {
        System.out.println("getPredictedLabels");


        Classifier classifier = new J48();
        try {
            classifier.buildClassifier(instances);
        } catch (Exception e) {
            assertTrue("Classifier could not be trained", false);
        }


        WekaModel instance = new WekaModel(classifier);

View Full Code Here

       throws Exception {


    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);

View Full Code Here

       throws Exception {


    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);

View Full Code Here

      throws Exception {


    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,

View Full Code Here

    instances.deleteWithMissingClass();
    
    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)

View Full Code Here

       throws Exception {


    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);

View Full Code Here

    instances.deleteWithMissingClass();
    
    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)

View Full Code Here

       throws Exception {


    ModelSelection modSelection;   


    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);

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
      m_root = new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds,

View Full Code Here

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