Package weka.classifiers.trees.j48

Examples of weka.classifiers.trees.j48.HTreeModelSelection

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

    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);
    }
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   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    return new NBTreeClassifierTree(null).getCapabilities();
  }
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  public void buildClassifier(Instances instances) throws Exception {
   
    NBTreeModelSelection modSelection =
      new NBTreeModelSelection(m_minNumObj, instances);

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