Sensitivity Calculator
Calculate sensitivity (true positive rate / recall) from true positive and false negative counts.
Inputs
Sensitivity (TPR)
0.89%
Actual Positives (TP + FN)
90
Step by step
Sensitivity: TP / (TP + FN)
80 ÷ (80 + 10)
= 0.8889
How it works
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
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.