类 InfoGainSplitCrit
java.lang.Object
weka.classifiers.trees.j48.SplitCriterion
weka.classifiers.trees.j48.EntropyBasedSplitCrit
weka.classifiers.trees.j48.InfoGainSplitCrit
- 所有已实现的接口:
Serializable
,RevisionHandler
Class for computing the information gain for a given distribution.
- 版本:
- $Revision: 1.10 $
- 作者:
- Eibe Frank (eibe@cs.waikato.ac.nz)
- 另请参阅:
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构造器概要
构造器 -
方法概要
修饰符和类型方法说明Returns the revision string.final double
splitCritValue
(Distribution bags) This method is a straightforward implementation of the information gain criterion for the given distribution.final double
splitCritValue
(Distribution bags, double totalNoInst) This method computes the information gain in the same way C4.5 does.final double
splitCritValue
(Distribution bags, double totalNoInst, double oldEnt) This method computes the information gain in the same way C4.5 does.从类继承的方法 weka.classifiers.trees.j48.EntropyBasedSplitCrit
logFunc, newEnt, oldEnt, splitEnt
从类继承的方法 weka.classifiers.trees.j48.SplitCriterion
splitCritValue, splitCritValue, splitCritValue
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构造器详细资料
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InfoGainSplitCrit
public InfoGainSplitCrit()
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方法详细资料
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splitCritValue
This method is a straightforward implementation of the information gain criterion for the given distribution.- 覆盖:
splitCritValue
在类中SplitCriterion
- 返回:
- value of splitting criterion. 0 by default
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splitCritValue
This method computes the information gain in the same way C4.5 does.- 参数:
bags
- the distributiontotalNoInst
- weight of ALL instances (including the ones with missing values).
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splitCritValue
This method computes the information gain in the same way C4.5 does.- 参数:
bags
- the distributiontotalNoInst
- weight of ALL instancesoldEnt
- entropy with respect to "no-split"-model.
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getRevision
Returns the revision string.- 返回:
- the revision
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