Examples of weka.classifiers.meta.ClassificationViaRegression

weka.classifiers.meta.ClassificationViaRegression

Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, see, for example

E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.

BibTeX:

 @article{Frank1998, author = {E. Frank and Y. Wang and S. Inglis and G. Holmes and I.H. Witten}, journal = {Machine Learning}, number = {1}, pages = {63-76}, title = {Using model trees for classification}, volume = {32}, year = {1998} }

Valid options are:

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

 -W Full name of base classifier. (default: weka.classifiers.trees.M5P)

  Options specific to classifier weka.classifiers.trees.M5P:

 -N Use unpruned tree/rules

 -U Use unsmoothed predictions

 -R Build regression tree/rule rather than a model tree/rule

 -M <minimum number of instances> Set minimum number of instances per leaf (default 4)

 -L Save instances at the nodes in the tree (for visualization purposes)

@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 1.27 $

  public boolean showInGUI() {
    return true;
  }
  
  public Classifier getClassifier() {
    ClassificationViaRegression c = new ClassificationViaRegression();
    c.setClassifier(new weka.classifiers.functions.LinearRegression());
    return (Classifier)c;
  }

  public boolean showInGUI() {
    return true;
  }
  
  public Classifier getClassifier() {
    ClassificationViaRegression c = new ClassificationViaRegression();
    c.setClassifier(new weka.classifiers.functions.LeastMedSq());
    return (Classifier)c;
  }

Examples of weka.classifiers.meta.ClassificationViaRegression

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