| 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.
|