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Class weka.classifiers.j48.InfoGainSplitCrit
java.lang.Object
|
+----weka.classifiers.j48.SplitCriterion
|
+----weka.classifiers.j48.EntropyBasedSplitCrit
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+----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)
InfoGainSplitCrit()
-
splitCritValue(Distribution)
- This method is a straightforward implementation of the information
gain criterion for the given distribution.
splitCritValue(Distribution, double)
- This method computes the information gain in the same way
C4.5 does.
splitCritValue(Distribution, double, double)
- This method computes the information gain in the same way
C4.5 does.
InfoGainSplitCrit
public InfoGainSplitCrit()
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
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).
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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