Naive Bayes and Text Classification I - Introduction and Theory

TL;DR

This paper introduces Naive Bayes classifiers, explaining their core concepts and theoretical foundation, particularly for document classification tasks, highlighting their simplicity and effectiveness.

Contribution

It provides an introductory overview of Naive Bayes theory and its application to text classification, filling a gap in accessible foundational knowledge.

Findings

Naive Bayes classifiers are simple and effective for document categorization.

They are based on Bayes' theorem with strong independence assumptions.

The paper emphasizes the theoretical underpinnings of Naive Bayes models.

Abstract

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this article, we will look at the main concepts of naive Bayes classification in the context of document categorization.