Bayes' Theorem and Naive Bayes

Key concepts:

• Bayes’ theorem, priors/posteriors, likelihood, evidence.
• Naive Bayes variants: Gaussian, Multinomial, Bernoulli.
• When naive assumptions still work well; calibration.

Common interview checks:

• Derive the decision rule for Multinomial NB.
• Effect of rare features and Laplace smoothing.