Package weka.classifiers.meta

Class ClassificationViaRegression

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.meta.ClassificationViaRegression
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
public class ClassificationViaRegression extends SingleClassifierEnhancer implements TechnicalInformationHandler, WeightedInstancesHandler
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)
Version:
$Revision: 15481 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
See Also: