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## weka.classifiers.trees.j48
Class InfoGainSplitCrit

    java.lang.Object
      weka.classifiers.trees.j48.SplitCriterion
          weka.classifiers.trees.j48.EntropyBasedSplitCrit
              weka.classifiers.trees.j48.InfoGainSplitCrit

All Implemented Interfaces:
  java.io.Serializable

    public final class InfoGainSplitCrit

extends EntropyBasedSplitCrit

Class for computing the information gain for a given distribution.

Version:
  $Revision: 1.6 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`InfoGainSplitCrit()`

Method Summary
`double` | `splitCritValue(Distribution bags)` This method is a straightforward implementation of the information gain criterion for the given distribution.
`double` | `splitCritValue(Distribution bags, double totalNoInst)` This method computes the information gain in the same way C4.5 does.
`double` | `splitCritValue(Distribution bags, double totalNoInst, double oldEnt)` This method computes the information gain in the same way C4.5 does.

Methods inherited from class weka.classifiers.trees.j48.EntropyBasedSplitCrit
`logFunc, newEnt, oldEnt, splitEnt`

Methods inherited from class weka.classifiers.trees.j48.SplitCriterion
`splitCritValue, splitCritValue, splitCritValue`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### InfoGainSplitCrit

    public InfoGainSplitCrit()

Method Detail

### 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`
  Returns:
    value of splitting criterion. 0 by default

### 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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