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java.lang.Object | +--weka.classifiers.Classifier | +--weka.classifiers.SingleClassifierEnhancer | +--weka.classifiers.meta.ClassificationViaRegression
Class for doing classification using regression methods. For more information, see
E. Frank, Y. Wang, S. Inglis, G. Holmes, and I.H. Witten (1998) "Using model trees for classification", Machine Learning, Vol.32, No.1, pp. 63-76.
Valid options are:
-W classname
Specify the full class name of a numeric predictor as the basis for
the classifier (required).
Constructor Summary | |
ClassificationViaRegression()
Default constructor. |
Method Summary | |
void |
buildClassifier(Instances insts)
Builds the classifiers. |
double[] |
distributionForInstance(Instance inst)
Returns the distribution for an instance. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
toString()
Prints the classifiers. |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
classifierTipText, getClassifier, getOptions, listOptions, setClassifier, setOptions |
Methods inherited from class weka.classifiers.Classifier |
classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
public ClassificationViaRegression()
Method Detail |
public java.lang.String globalInfo()
public void buildClassifier(Instances insts) throws java.lang.Exception
buildClassifier
in class Classifier
insts
- the training data.
java.lang.Exception
- if a classifier can't be builtpublic double[] distributionForInstance(Instance inst) throws java.lang.Exception
distributionForInstance
in class Classifier
inst
- the instance to be classified
java.lang.Exception
- if the distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] argv)
argv
- the options for the learner
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Copyright (c) 2003 David Lindsay, Computer Learning Research Centre, Dept. Computer Science, Royal Holloway, University of London