24. Naive Bayes Classification with Python

By Bernd Klein. Last modified: 19 Apr 2024.

Definition

In machine learning, a Bayes classifier is a simple probabilistic classifier, which is based on applying Bayes' theorem. The feature model used by a naive Bayes classifier makes strong independence assumptions. This means that the existence of a particular feature of a class is independent or unrelated to the existence of every other feature.

Definition of independent events:

Two events E and F are independent, if both E and F have positive probability and if P(E|F) = P(E) and P(F|E) = P(F)

As we have stated in our definition, the Naive Bayes Classifier is based on the Bayes' theorem. The Bayes theorem is based on the conditional probability, which we will define now:

Live Python training

Enjoying this page? We offer live Python training courses covering the content of this site.

See our Python training courses