Why does Naive Bayes work so well despite an assumption that is almost always false?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
