Examples of weka.classifiers.trees.J48

weka.classifiers.trees.j48.NBTreeModelSelection

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 $

   *
   * @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 );

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 );

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    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();
    
    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


    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();
    
    ModelSelection modSelection;   


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

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, m_useMDLcorrection);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);

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, true);
    }

View Full Code Here

   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    return new NBTreeClassifierTree(null).getCapabilities();
  }

View Full Code Here

  public void buildClassifier(Instances instances) throws Exception {
    
    NBTreeModelSelection modSelection = 
      new NBTreeModelSelection(m_minNumObj, instances);


    m_root = new NBTreeClassifierTree(modSelection);
    m_root.buildClassifier(instances);
  }

View Full Code Here

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


    m_root = new NBTreeClassifierTree(modSelection);
    m_root.buildClassifier(instances);
  }

View Full Code Here

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