// ◇ Classical Machine Learning
Naive Bayes
Applies Bayes’ rule assuming features are independent given the class.
朴素贝叶斯 1997 Beginner core
IN PLAIN WORDS
Count how likely each feature is under each class, then at prediction time multiply those probabilities with the class prior and pick the largest. The independence assumption is almost never true, yet it trains instantly, works with little data, and has long been a strong text-classification baseline.
Used in: Spam filtering, text-classification baselines
01Original paper
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Pedro Domingos, Michael Pazzani · Machine Learning · 1997
DOI ↗ ✓ Verified · Crossref API doi:10.1023/A:1007413511361
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
- No prerequisites — a good starting point
Then you can learn
- Nothing in the atlas builds on it yet
Related:Logistic Regressionk-Nearest Neighbors
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
For AI / data
- Verification: verified, checked 2026-10-22.
- JSON: /api/learn/algorithms.json (id:
naive-bayes)