Example of a naive Bayes classifier depicted as a Bayesian Network
In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.[1]
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression,[2]: 718 which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.
In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes.[3] All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.[2][3]
^McCallum, Andrew. "Graphical Models, Lecture2: Bayesian Network Representation" (PDF). Archived (PDF) from the original on 2022-10-09. Retrieved 22 October 2019.
^ abRussell, Stuart; Norvig, Peter (2003) [1995]. Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. ISBN 978-0137903955.
^ abHand, D. J.; Yu, K. (2001). "Idiot's Bayes — not so stupid after all?". International Statistical Review. 69 (3): 385–399. doi:10.2307/1403452. ISSN 0306-7734. JSTOR 1403452.
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