Bayes' Theorem and Naive Bayes Classifier

Berrar, Daniel (2024). Bayes' Theorem and Naive Bayes Classifier. In: Ranganathan, Shoba; Cannataro, Mario and Asif, Mohammed eds. Encyclopedia of Bioinformatics and Computational Biology, 2nd edition. Elsevier (In Press).


This article gives a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from the fundamental elements of probability theory, this article outlines all steps leading to one of the oldest workhorses of machine learning: the naive Bayes classifier. As a tutorial, the text enables novice practitioners to quickly understand the essential concepts. As an encyclopedic article, the text provides a complete reference for bioinformaticians, computer scientists, and statisticians, with an illustration of some caveats and pitfalls—and how to avoid them—in building a naive Bayes classifier in the R programming language.

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