Which metric is defined as the proportion of real positive events that are captured?

Which metric is defined as the proportion of real positive events that are captured?

• A. Recall (Sensitivity)
• B. Precision
• C. F1 Score
• D. AUC

Answer: (A)

Recall, or sensitivity, is the proportion of real positive events that the model correctly identifies. It is calculated as true positives divided by the sum of true positives and false negatives. For example, if there are 100 actual positives and the model detects 85 of them, recall is 0.85, meaning 85% of real positives are captured. A high recall means the model misses few positives, but it can come at the cost of lower precision if many non-positives are also flagged as positives. Contrast this with precision, which focuses on how many of the predicted positives are truly positive, and with F1, which combines precision and recall. AUC measures the model’s ability to rank positives higher than negatives across thresholds, not the fixed-proportion capture at a given threshold. The described metric is recall.

Recall, or sensitivity, is the proportion of real positive events that the model correctly identifies. It is calculated as true positives divided by the sum of true positives and false negatives. For example, if there are 100 actual positives and the model detects 85 of them, recall is 0.85, meaning 85% of real positives are captured. A high recall means the model misses few positives, but it can come at the cost of lower precision if many non-positives are also flagged as positives. Contrast this with precision, which focuses on how many of the predicted positives are truly positive, and with F1, which combines precision and recall. AUC measures the model’s ability to rank positives higher than negatives across thresholds, not the fixed-proportion capture at a given threshold. The described metric is recall.