Naive Bayes Algorithm Explained: Basics, Example & Python

Naive Bayes is mainly used for classification problems. Typical examples include email spam detection, sentiment analysis, document categorisation and simple recommendation systems where inputs can be represented as feature counts or categories.

It is called naive because it assumes that all features are conditionally independent given the class. In real world data this is rarely strictly true, but the assumption simplifies the maths and still gives good results in many practical situations.

Yes. Even with modern deep learning models, Naive Bayes remains a strong baseline in 2026, especially for small datasets and text data. It trains quickly, is easy to interpret, and is often used as a first model when exploring a new classification problem.