Precision asks how many of your positive predictions were correct. Recall asks how many of the real positives you actually caught. Both put true positives on top; only the denominator changes.
Precision divides true positives by every case you flagged, so false positives hurt it. Recall divides true positives by every case that was truly positive, so misses hurt it. Neither metric counts true negatives, which is why both survive when negatives vastly outnumber positives.
// flagged 100 items, 80 were right; 200 real positives exist
const precision = 80 / 100; // 0.80
const recall = 80 / 200; // 0.40
Reading them together stops a common trap. A model that flags one obvious case scores perfect precision and almost no recall. A model that flags everything scores perfect recall and terrible precision. Neither number means much alone, so quote both, plus how many predictions produced them.
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