Bayes' Theorem and Naive Bayes Classifier

By Daniel Peter Berrar. Encyclopedia of Bioinformatics and Computational Biology, 2019

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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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dbba16b6c214644bf5b5820e98db0f7d87582113