naive bayes

A from-scratch staple that checks whether you grasp the conditional-independence assumption and why you work in log space. The signal is fitting per-class means and variances, summing log-likelihoods, and adding the log prior. Here is the implementation.

Two simple classifiers that keep showing up in interviews because they check whether you grasp assumptions and tradeoffs, not just sklearn calls. What matters is k-NN's laziness and curse of dimensionality against Naive Bayes' independence assumption. Here is the answer.

The 'naive' independence assumption is wrong on real data, yet the classifier remains a strong baseline for text. The interesting answer explains why classification survives a broken assumption, plus smoothing and the variants. Here it is.