naive bayes

A from-scratch staple that checks whether you understand 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 still appear in interviews because they test whether you understand assumptions and tradeoffs, not just sklearn calls. The signal is k-NN's laziness and curse of dimensionality versus Naive Bayes' independence assumption. Here is the answer.

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