Recall Calculator
Calculate recall (sensitivity) from true positives and false negatives.
About Recall Calculator
Recall, also called sensitivity or true positive rate (TPR), measures how many of the actual positive cases the model correctly identified: recall = TP / (TP + FN). High recall means the model misses few real positives. Recall says nothing about false positives; it's the mirror image of precision, and the two are typically reported together because improving one often comes at the expense of the other, depending on the classification threshold.
Formula
recall = TP / (TP + FN)
- TP
- True positives
- FN
- False negatives
Worked example
With the default values below, Recall Calculator returns Recall (Sensitivity) of 0.89%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- True Positives (TP)accepts 0–1,000,000,000
- 80
- False Negatives (FN)accepts 0–1,000,000,000
- 10
Results
- Recall (Sensitivity)
- 0.89%
- Actual Positives (TP + FN)
- 90
Step by step
- 1Recall: TP / (TP + FN)= 0.8889
Assumptions and limits
- The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
When should I prioritize recall over precision?
Prioritize recall when false negatives are costly — for example, missing a cancer diagnosis or failing to detect fraud — where catching every real positive matters more than occasionally flagging a false alarm.
Is recall the same as sensitivity?
Yes, recall, sensitivity, and true positive rate (TPR) are all names for the same metric: TP / (TP + FN). The term used often depends on the field — 'sensitivity' is common in medical diagnostics, 'recall' in information retrieval and ML.
How does the classification threshold affect recall?
Lowering the decision threshold for classifying something as positive typically increases recall (catching more true positives) but also increases false positives, reducing precision — this tradeoff is visualized by the ROC and precision-recall curves.