Sensitivity Calculator
Calculate sensitivity, the true positive rate, from a confusion matrix.
About Sensitivity Calculator
Sensitivity, also called the true positive rate (TPR) or recall, measures how many of the actual positive cases the model correctly caught: sensitivity = TP / (TP + FN). It's mathematically identical to recall — the different name is conventional in medical and diagnostic testing contexts, while 'recall' is more common in machine learning and information retrieval. Sensitivity and specificity together form the two axes of the ROC curve and fully characterize a binary classifier's tradeoffs.
Formula
sensitivity = TP / (TP + FN)
- TP
- True positives
- FN
- False negatives
Worked example
With the default values below, Sensitivity Calculator returns Sensitivity (TPR) 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
- Sensitivity (TPR)
- 0.89%
- Actual Positives (TP + FN)
- 90
Step by step
- 1Sensitivity: 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
Is sensitivity the same formula as recall?
Yes, sensitivity and recall are computed identically as TP / (TP + FN); the terminology difference is purely conventional across fields — 'sensitivity' in medicine/diagnostics, 'recall' in ML/information retrieval.
What does 100% sensitivity mean?
100% sensitivity means the test/model correctly identified every single true positive case, with zero false negatives — though this alone says nothing about how many false positives it also produced.
How do sensitivity and specificity trade off?
Adjusting a model's decision threshold to increase sensitivity (catching more true positives) typically decreases specificity (more false alarms among negatives), and vice versa — the ROC curve visualizes this tradeoff across all thresholds.