juniorNaïve Bayes
What is Bayes’ theorem in machine learning?
Updated May 17, 2026
Short answer
Bayes’ theorem describes how to update probabilities based on new evidence.
Deep explanation
Bayes’ theorem states: P(C|X) = P(X|C)P(C) / P(X). It allows models to update prior beliefs (P(C)) using observed evidence (X). Naïve Bayes classifiers rely on this principle to compute posterior probabilities for classification tasks.
Real-world example
Medical diagnosis updating disease probability based on symptoms.
Common mistakes
- Confusing likelihood with posterior probability.
Follow-up questions
- What is prior probability?
- What is likelihood?