Anomaly Detection
Finding rare rows that do not belong to normal patterns, often a fraud or failure class, scored by precision and recall rather than accuracy alone.
With a rare base rate, a detector that catches most real anomalies can score lower raw accuracy than a baseline that flags nothing, since accuracy averages over both classes and the majority class's size dominates. Precision and recall, scored on the rare class specifically, are what actually measure whether a detector is doing its job.
Statistical, proximity-based, and model-based methods all assume normal behavior is common and anomalous behavior is rare, differing only in how they characterize that norm.