(c) Recall — Recall is defined as the fraction of positive cases that are correctly identified by the model.
Recall (also called sensitivity) = TP / (TP + FN). It measures how many actual positives were correctly caught. Precision focuses on correctness of positive predictions; Accuracy covers overall correct predictions; F1 balances precision and recall.