| java.lang.Object weka.classifiers.trees.j48.SplitCriterion weka.classifiers.trees.j48.EntropyBasedSplitCrit weka.classifiers.trees.j48.InfoGainSplitCrit
InfoGainSplitCrit | final public class InfoGainSplitCrit extends EntropyBasedSplitCrit (Code) | | Class for computing the information gain for a given distribution.
author: Eibe Frank (eibe@cs.waikato.ac.nz) version: $Revision: 1.9 $ |
Method Summary | |
final public double | splitCritValue(Distribution bags) This method is a straightforward implementation of the information
gain criterion for the given distribution. | final public double | splitCritValue(Distribution bags, double totalNoInst) This method computes the information gain in the same way
C4.5 does. | final public double | splitCritValue(Distribution bags, double totalNoInst, double oldEnt) This method computes the information gain in the same way
C4.5 does. |
splitCritValue | final public double splitCritValue(Distribution bags)(Code) | | This method is a straightforward implementation of the information
gain criterion for the given distribution.
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splitCritValue | final public double splitCritValue(Distribution bags, double totalNoInst)(Code) | | This method computes the information gain in the same way
C4.5 does.
Parameters: bags - the distribution Parameters: totalNoInst - weight of ALL instances (including theones with missing values). |
splitCritValue | final public double splitCritValue(Distribution bags, double totalNoInst, double oldEnt)(Code) | | This method computes the information gain in the same way
C4.5 does.
Parameters: bags - the distribution Parameters: totalNoInst - weight of ALL instances Parameters: oldEnt - entropy with respect to "no-split"-model. |
Fields inherited from weka.classifiers.trees.j48.EntropyBasedSplitCrit | protected static double log2(Code)(Java Doc)
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