recall

Every classifier and every LLM-as-judge eval reduces to a confusion matrix, but the screen is whether you handle the zero-denominator cases without crashing and can say which metric matters for the problem at hand.

A confusion matrix is the 2x2 of right and wrong predictions per class, and it is the reason a 99% accurate fraud model can be useless. The worked imbalance example, and how reading the matrix as precision and recall fixes the lie.

Ten X-ray studies a second, a 2-second budget, and a missed cancer is unacceptable. The hard parts are recall-first thresholding, a monitor that watches the radiologist, and surviving a new scanner.