What does recall measure?

Correct answer: The proportion of actual positive cases that are correctly identified as positive

Direct answer: A is correct. Recall asks, “Of all the cases that are truly positive, how many did the model find?” It is calculated as TP ÷ (TP + FN). Here, TP means true positives, or positive cases correctly identified, and FN means false negatives, or positive cases missed by the model. A high recall means that the model misses relatively few actual positive cases. Option B describes precision: among the cases predicted positive, it asks how many are truly positive, using TP ÷ (TP + FP). Option C describes accuracy, which considers all correct predictions among all cases. Option D describes specificity, which measures how well actual negative cases are identified. For a disease-screening example, recall is especially concerned with finding people who really have the disease. Memory cue: recall recalls the real positives; its denominator contains all actual positives, TP plus FN.