# Package weka.classifiers.trees.j48

package weka.classifiers.trees.j48

- Related Packages
  Package
  Description
  weka.classifiers.trees
  weka.classifiers.trees.ht
  weka.classifiers.trees.lmt
  weka.classifiers.trees.m5
- Classes
  Class
  Description
  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.
  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.
  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.