Bayes’ Theorem and Naive Bayes Classifier

Berrar, Daniel (2019). Bayes’ Theorem and Naive Bayes Classifier. In: Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian and Cannataro, Mario eds. Encyclopedia of Bioinformatics and Computational Biology. Reference Module in Life Sciences, 1. Elsevier, pp. 403–412.

DOI: https://doi.org/10.1016/B978-0-12-809633-8.20473-1

Abstract

The goal of this article is to give a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from fundamental elements of probability theory, this text 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, machine learners, 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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