|
Class Summary |
| ADNode |
The ADNode class implements the ADTree datastructure which increases
the speed with which sub-contingency tables can be constructed from
a data set in an Instances object. |
| AODE |
AODE achieves highly accurate classification by averaging over all
of a small space of alternative naive-Bayes-like models that have
weaker (and hence less detrimental) independence assumptions than
naive Bayes. |
| BayesNet |
Base class for a Bayes Network classifier. |
| BayesNetB |
Class for a Bayes Network classifier based on a hill climbing algorithm for
learning structure as described in Buntine, W. (1991). |
| BayesNetB2 |
Class for a Bayes Network classifier based on Buntines hill climbing algorithm for
learning structure, but augmented to allow arc reversal as an operation.
|
| BayesNetK2 |
Class for a Bayes Network classifier based on K2 for learning structure.
|
| ComplementNaiveBayes |
Class for building and using a Complement class Naive Bayes classifier.
|
| DiscreteEstimatorBayes |
Symbolic probability estimator based on symbol counts and a prior. |
| NaiveBayes |
Class for a Naive Bayes classifier using estimator classes. |
| NaiveBayesMultinomial |
The core equation for this classifier:
P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)
where Ci is class i and D is a document |
| NaiveBayesSimple |
Class for building and using a simple Naive Bayes classifier.
|
| NaiveBayesUpdateable |
Class for a Naive Bayes classifier using estimator classes. |
| ParentSet |
Helper class for Bayes Network classifiers. |
| VaryNode |
Part of ADTree implementation. |