What is Naive Bayes?

What is Naive Bayes?

Approach: Explain the application of Bayes' Theorem with independence assumptions.

A probabilistic classifier based on Bayes' Theorem. It is called 'naive' because it assumes that all features are independent of each other given the class label. Despite this oversimplification, it often performs well for text classification.

Follow-up: What happens if a feature value in the test set never appeared in the training set?

Key concepts: conditional independence, prior probability, posterior.