Uses of Class
weka.classifiers.trees.j48.Distribution

Packages that use Distribution

weka.classifiers.rules.part
weka.classifiers.trees.j48

Uses of Distribution in weka.classifiers.rules.part

Fields in weka.classifiers.rules.part declared as Distribution

protected Distribution

ClassifierDecList.m_test The pruning instances.

Uses of Distribution in weka.classifiers.trees.j48

Fields in weka.classifiers.trees.j48 declared as Distribution

protected Distribution	ClassifierSplitModel.m_distribution Distribution of class values.
protected Distribution	ClassifierTree.m_test The pruning instances.

Methods in weka.classifiers.trees.j48 that return Distribution

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.

Methods in weka.classifiers.trees.j48 with parameters of type Distribution

double	GainRatioSplitCrit.splitCritValue(Distribution bags) This method is a straightforward implementation of the gain ratio criterion for the given distribution.
double	GainRatioSplitCrit.splitCritValue(Distribution bags, double totalnoInst, double numerator) This method computes the gain ratio in the same way C4.5 does.
private double	GainRatioSplitCrit.splitEnt(Distribution bags, double totalnoInst) Help method for computing the split entropy.
double	InfoGainSplitCrit.splitCritValue(Distribution bags) This method is a straightforward implementation of the information gain criterion for the given distribution.
double	InfoGainSplitCrit.splitCritValue(Distribution bags, double totalNoInst) This method computes the information gain 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	EntropySplitCrit.splitCritValue(Distribution bags) Computes entropy for given distribution.
double	EntropySplitCrit.splitCritValue(Distribution train, Distribution test) Computes entropy of test distribution with respect to training distribution.
private double	C45PruneableClassifierTree.getEstimatedErrorsForDistribution(Distribution theDistribution) Computes estimated errors for leaf.
double	SplitCriterion.splitCritValue(Distribution bags) Computes result of splitting criterion for given distribution.
double	SplitCriterion.splitCritValue(Distribution train, Distribution test) Computes result of splitting criterion for given training and test distributions.
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	SplitCriterion.splitCritValue(Distribution train, Distribution test, Distribution defC) Computes result of splitting criterion for given training and test distributions and given default distribution.
Distribution	Distribution.subtract(Distribution toSubstract) Subtracts the given distribution from this one.
double	EntropyBasedSplitCrit.oldEnt(Distribution bags) Computes entropy of distribution before splitting.
double	EntropyBasedSplitCrit.newEnt(Distribution bags) Computes entropy of distribution after splitting.
double	EntropyBasedSplitCrit.splitEnt(Distribution bags) Computes entropy after splitting without considering the class values.

Constructors in weka.classifiers.trees.j48 with parameters of type Distribution

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.