Package | Description |
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weka.classifiers.trees.j48 |
Modifier and Type | Method and Description |
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Distribution |
ClassifierSplitModel.distribution()
Returns the distribution of class values induced by the model.
|
Distribution |
Distribution.subtract(Distribution toSubstract)
Subtracts the given distribution from this one.
|
Modifier and Type | Method and Description |
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double |
EntropyBasedSplitCrit.newEnt(Distribution bags)
Computes entropy of distribution after splitting.
|
double |
EntropyBasedSplitCrit.oldEnt(Distribution bags)
Computes entropy of distribution before splitting.
|
double |
GainRatioSplitCrit.splitCritValue(Distribution bags)
This method is a straightforward implementation of the gain
ratio criterion for the given distribution.
|
double |
SplitCriterion.splitCritValue(Distribution bags)
Computes result of splitting criterion for given distribution.
|
double |
InfoGainSplitCrit.splitCritValue(Distribution bags)
This method is a straightforward implementation of the information
gain criterion for the given distribution.
|
double |
EntropySplitCrit.splitCritValue(Distribution bags)
Computes entropy for given distribution.
|
double |
SplitCriterion.splitCritValue(Distribution train,
Distribution test)
Computes result of splitting criterion for given training and
test distributions.
|
double |
EntropySplitCrit.splitCritValue(Distribution train,
Distribution test)
Computes entropy of test distribution with respect to training distribution.
|
double |
SplitCriterion.splitCritValue(Distribution train,
Distribution test,
Distribution defC)
Computes result of splitting criterion for given training and
test distributions and given default distribution.
|
double |
SplitCriterion.splitCritValue(Distribution train,
Distribution test,
int noClassesDefault)
Computes result of splitting criterion for given training and
test distributions and given number of classes.
|
double |
InfoGainSplitCrit.splitCritValue(Distribution bags,
double totalNoInst)
This method computes the information gain in the same way
C4.5 does.
|
double |
GainRatioSplitCrit.splitCritValue(Distribution bags,
double totalnoInst,
double numerator)
This method computes the gain ratio in the same way C4.5 does.
|
double |
InfoGainSplitCrit.splitCritValue(Distribution bags,
double totalNoInst,
double oldEnt)
This method computes the information gain in the same way
C4.5 does.
|
double |
EntropyBasedSplitCrit.splitEnt(Distribution bags)
Computes entropy after splitting without considering the
class values.
|
Distribution |
Distribution.subtract(Distribution toSubstract)
Subtracts the given distribution from this one.
|
Constructor and Description |
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Distribution(Distribution toMerge)
Creates distribution with only one bag by merging all
bags of given distribution.
|
Distribution(Distribution toMerge,
int index)
Creates distribution with two bags by merging all bags apart of
the indicated one.
|
NoSplit(Distribution distribution)
Creates "no-split"-split for given distribution.
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