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Class weka.classifiers.j48.InfoGainSplitCrit

java.lang.Object
    |
    +----weka.classifiers.j48.SplitCriterion
            |
            +----weka.classifiers.j48.EntropyBasedSplitCrit
                    |
                    +----weka.classifiers.j48.InfoGainSplitCrit

public final class InfoGainSplitCrit
extends EntropyBasedSplitCrit
Class for computing the information gain for a given distribution.

Version:
$Revision: 1.5 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)

Constructor Index

 o InfoGainSplitCrit()
 

Method Index

 o splitCritValue(Distribution)
This method is a straightforward implementation of the information gain criterion for the given distribution.
 o splitCritValue(Distribution, double)
This method computes the information gain in the same way C4.5 does.
 o splitCritValue(Distribution, double, double)
This method computes the information gain in the same way C4.5 does.

Constructor Detail

 o InfoGainSplitCrit
public InfoGainSplitCrit()

Method Detail

 o splitCritValue
public final double splitCritValue(Distribution bags)
          This method is a straightforward implementation of the information gain criterion for the given distribution.
Overrides:
splitCritValue in class SplitCriterion
 o splitCritValue
public final double splitCritValue(Distribution bags,
                                   double totalNoInst)
          This method computes the information gain in the same way C4.5 does.
Parameters:
distribution - the distribution
totalNoInst - weight of ALL instances (including the ones with missing values).
 o splitCritValue
public final double splitCritValue(Distribution bags,
                                   double totalNoInst,
                                   double oldEnt)
          This method computes the information gain in the same way C4.5 does.
Parameters:
distribution - the distribution
totalNoInst - weight of ALL instances
oldEnt - entropy with respect to "no-split"-model.

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