weka.classifiers.trees.j48

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## Package weka.classifiers.trees.j48

Class Summary
BinC45ModelSelection | Class for selecting a C4.5-like binary (!) split for a given dataset.
BinC45Split | Class implementing a binary C4.5-like split on an attribute.
C45ModelSelection | Class for selecting a C4.5-type split for a given dataset.
C45PruneableClassifierTree | Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTreeG | Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.
C45Split | Class implementing a C4.5-type split on an attribute.
ClassifierSplitModel | Abstract class for classification models that can be used recursively to split the data.
ClassifierTree | Class for handling a tree structure used for classification.
Distribution | Class for handling a distribution of class values.
EntropyBasedSplitCrit | "Abstract" class for computing splitting criteria based on the entropy of a class distribution.
EntropySplitCrit | Class for computing the entropy for a given distribution.
GainRatioSplitCrit | Class for computing the gain ratio for a given distribution.
GraftSplit | Class implementing a split for nodes added to a tree during grafting.
InfoGainSplitCrit | Class for computing the information gain for a given distribution.
ModelSelection | Abstract class for model selection criteria.
NBTreeClassifierTree | Class for handling a naive bayes tree structure used for classification.
NBTreeModelSelection | Class for selecting a NB tree split.
NBTreeNoSplit | Class implementing a "no-split"-split (leaf node) for naive bayes trees.
NBTreeSplit | Class implementing a NBTree split on an attribute.
NoSplit | Class implementing a "no-split"-split.
PruneableClassifierTree | Class for handling a tree structure that can be pruned using a pruning set.
SplitCriterion | Abstract class for computing splitting criteria with respect to distributions of class values.
Stats | Class implementing a statistical routine needed by J48 to compute its error estimate.

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