Naive Bayes Classifier
The naive Bayes classifier is a probabilistic classifier simplifying Bayes' theorem by naively assuming class conditional independence. Although this assumption leads to biased posterior probabilities, the ordered probabilities of Naive Bayes result in a classification performance comparable to that of classification trees and neural networks. Notwithstanding Naive Bayes' popularity due to its simplicity combined with high accuracy and speed, its conditional independence assumption rarely holds. There are mainly two approaches to alleviate this naivety:
- Selecting attribute subsets in which attributes are conditionally independent (cf. Selective Bayesian Classifier ).
- Extending the structure of Naive Bayes to represent attribute dependencies (cf. Averaged One-Dependence Estimators (AODE) ).
Read more about this topic: Random Naive Bayes
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