Naïve Bayes Classifiers

In this article we will talk about Multinomial Naïve Bayes Classifier, so called because it is a Bayesian Classifier that makes a simplifying (naïve) assumption about the interaction b/w features.

Let’s understand the intuition of this classifier in the context of text classification. Given a text document we first represent the text document as a bag-of-words (i.e. an unordered set of words in the document with their position information removed) keeping only the word-frequency in the given document. In this bag-of-words representation, all we care about is how many times a given word appears in this document.

Naïve Bayes is a probabilistic classifier, meaning that for a given document d, out of all classes $c\in C$ the classifier returns the class $\hat{c}$ which has the maximum posterior probability given the document d.