50 caught out of 55 actual positives
tp = 50fp = 10fn = 5tn = 35
Result: Recall 0.9091 · False negative rate 0.0909
Recall Calculator
Recall answers "of all the positive cases that existed, how many did the model catch?" It is TP ÷ (TP + FN) and is the metric to watch when missing a positive is costly — cancer screening, security alerts, churn detection. Recall and sensitivity are the same formula under different names.
Enter your values and press Calculate — the result appears here.
Worked examples
Every positive found
tp = 40fp = 5fn = 0tn = 55
Result: Recall 1.00
Frequently asked questions
What does recall tell me?
Recall is the coverage of the positive class: of the cases that truly were positive, the fraction the model identified. High recall means few positives slipped through unnoticed.
When is recall the right metric?
When a missed positive is the worst outcome — a tumor the scan fails to flag, a fraud transaction that goes through, a failing student the system does not catch. There you accept more false alarms to raise recall.
Is recall the same as sensitivity?
Yes. Sensitivity, recall, and the true positive rate all compute TP ÷ (TP + FN). Different fields prefer different names: medicine says sensitivity, information retrieval says recall.
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Sensitivity Calculator
Compute sensitivity (recall) — the share of actual positives correctly detected by a test or model.
Try it now →Precision Calculator
Compute precision — the share of positive predictions that were actually correct.
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Compute the F1 score — the harmonic mean of precision and recall — from TP, FP, FN, TN.
Try it now →Accuracy Calculator
Calculate classification accuracy from true/false positives and negatives.
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