Text Classification Using Naive Bayes: Theory & A Working Example

Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. The dataset is divided into two parts, namely, feature matrix and the response/target vector. Side Note: The assumptions made by Naive Bayes are not generally correct in real-world situations. In-fact, the independence assumption is often not meet and this is why it is called "Naive" i.e. because it assumes something that might not be true.