ML Math Derivations (9): Naive Bayes

Wed, 28 Jan 2026 09:00:00 +0000

Hook: A spam filter that trains in milliseconds, scales to a million features, has no hyperparameters worth tuning, and still beats much fancier models on short-text problems. Naive Bayes pulls this off by making one outrageous assumption — every feature is independent given the class — and refusing to apologise for it. The assumption is wrong on essentially every real dataset, yet the classifier works. Understanding why is a tour through generative modelling, MAP estimation, Dirichlet priors, and the bias–variance tradeoff. This article walks the entire path.

Naive Bayes on Chen Kai Blog

ML Math Derivations (9): Naive Bayes