text classification

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.

Tagging articles at scale is bread-and-butter ML, but the naive version fails three ways: it is multi-label, the taxonomy is hierarchical and keeps growing, and rare classes hide behind aggregate accuracy. What matters is the model choice plus how you handle all three.