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Class weka.classifiers.j48.InfoGainSplitCrit
java.lang.Object
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+----weka.classifiers.j48.SplitCriterion
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+----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.
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz)
-
InfoGainSplitCrit()
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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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