Bayes and Naive Bayes Classifier

TL;DR

This paper discusses Bayesian and Naive Bayes classifiers, emphasizing their probabilistic foundations, robustness to noise, and practical applications in statistical classification tasks.

Contribution

It provides an overview of Bayesian classification, highlighting its principles, advantages, and the simplicity of the Naive Bayes approach for practical use.

Findings

Bayesian classifiers incorporate prior knowledge and observed data.

Naive Bayes is robust to noisy input data.

Bayes classifier minimizes misclassification probability.

Abstract

The Bayesian Classification represents a supervised learning method as well as a statistical method for classification. Assumes an underlying probabilistic model and it allows us to capture uncertainty about the model in a principled way by determining probabilities of the outcomes. This Classification is named after Thomas Bayes (1702-1761), who proposed the Bayes Theorem. Bayesian classification provides practical learning algorithms and prior knowledge and observed data can be combined. Bayesian Classification provides a useful perspective for understanding and evaluating many learning algorithms. It calculates explicit probabilities for hypothesis and it is robust to noise in input data. In statistical classification the Bayes classifier minimises the probability of misclassification. That was a visual intuition for a simple case of the Bayes classifier, also called: 1)Idiot Bayes 2)Naive Bayes 3)Simple Bayes