Examples of weka.experiment.ClassifierSplitEvaluator

weka.experiment.ClassifierSplitEvaluator

A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.

Valid options are:

 -W <class name> The full class name of the classifier. eg: weka.classifiers.bayes.NaiveBayes

 -C <index> The index of the class for which IR statistics are to be output. (default 1)

 -I <index> The index of an attribute to output in the results. This attribute should identify an instance in order to know which instances are in the test set of a cross validation. if 0 no output (default 0).

 -P Add target and prediction columns to the result for each fold.

  Options specific to classifier weka.classifiers.rules.ZeroR:

 -D If set, classifier is run in debug mode and may output additional info to the console

All options after -- will be passed to the classifier. @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5987 $

    m_NewBut.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    Experiment newExp = new Experiment();
    CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
    cvrp.setNumFolds(10);
    cvrp.setSplitEvaluator(new ClassifierSplitEvaluator());
    newExp.setResultProducer(cvrp);
    newExp.setPropertyArray(new Classifier[0]);
    newExp.setUsePropertyIterator(true);
    setExperiment(newExp);

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    }


    SplitEvaluator se = null;
    Classifier sec = null;
    if (m_ExpClassificationRBut.isSelected()) {
      se = new ClassifierSplitEvaluator();
      sec = ((ClassifierSplitEvaluator)se).getClassifier();
    } else {
      se = new RegressionSplitEvaluator();
      sec = ((RegressionSplitEvaluator)se).getClassifier();
    }

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    m_NewBut.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    Experiment newExp = new Experiment();
    CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
    cvrp.setNumFolds(10);
    cvrp.setSplitEvaluator(new ClassifierSplitEvaluator());
    newExp.setResultProducer(cvrp);
    newExp.setPropertyArray(new Classifier[0]);
    newExp.setUsePropertyIterator(true);
    setExperiment(newExp);

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    }


    SplitEvaluator se = null;
    Classifier sec = null;
    if (m_ExpClassificationRBut.isSelected()) {
      se = new ClassifierSplitEvaluator();
      sec = ((ClassifierSplitEvaluator)se).getClassifier();
    } else {
      se = new RegressionSplitEvaluator();
      sec = ((RegressionSplitEvaluator)se).getClassifier();
    }

View Full Code Here

    m_NewBut.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    Experiment newExp = new Experiment();
    CrossValidationResultProducer cvrp = new CrossValidationResultProducer();
    cvrp.setNumFolds(10);
    cvrp.setSplitEvaluator(new ClassifierSplitEvaluator());
    newExp.setResultProducer(cvrp);
    newExp.setPropertyArray(new Classifier[0]);
    newExp.setUsePropertyIterator(true);
    setExperiment(newExp);

View Full Code Here

    }


    SplitEvaluator se = null;
    Classifier sec = null;
    if (m_ExpClassificationRBut.isSelected()) {
      se = new ClassifierSplitEvaluator();
      sec = ((ClassifierSplitEvaluator)se).getClassifier();
    } else {
      se = new RegressionSplitEvaluator();
      sec = ((RegressionSplitEvaluator)se).getClassifier();
    }

View Full Code Here

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