Why a 99%-accurate model can still be completely useless — and which of these three numbers actually tells you that.
Accuracy sounds like the obvious scorecard for a model: how often is it right? But "right" blends two very different kinds of correctness together, and when the thing you're trying to detect is rare, that blend can hide a model that is doing essentially nothing useful. Precision and recall exist because sometimes you need to know, specifically, how a model is right or wrong — not just how often.