What are precision and recall, and when do you favour one?

Precision = of the items predicted positive, how many were truly positive (TP / (TP+FP)). Recall = of all actual positives, how many did we catch (TP / (TP+FN)). Favour precision when false positives are costly (e.g. flagging a legit transaction as fraud annoys customers); favour recall when missing a positive is costly (e.g. cancer screening). The F1 score balances the two. Which matters depends on the real-world cost of each error type.