About Sensitivity and Specificity Calculator
This sensitivity and specificity calculator works from a two-by-two table of a diagnostic test or classifier and returns both rates, along with precision, accuracy and the related metrics. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.
Sensitivity is the share of people with the condition that the test detects, which is the same quantity machine learning calls recall. Specificity is the share of people without it that the test correctly clears. A screening test wants high sensitivity so it misses few cases; a confirmatory test wants high specificity so it raises few false alarms.
These two do not depend on how common the condition is, but the chance that a positive result is correct (precision, also called positive predictive value) does. The example has 100 diseased and 200 healthy people.
How to use Sensitivity and Specificity Calculator
- Choose "Matrix counts" and set the number of classes, or choose "Actual and predicted labels" and paste the two lists.
- In matrix mode, rows are the actual class and columns the predicted class. Edit the cells and the class names.
- Pick the headline metric: accuracy, precision, recall, specificity, F1, F-beta, MCC, kappa or balanced accuracy.
- For precision, recall and F-scores, choose which class counts as positive. With two classes the first class is the positive one.
- Read the headline value and its explanation, then scroll to the per-class table, the averages and the worked steps.
- Use the copy button to take the metrics into your notes or report.
Features
- Binary and multi-class matrices up to 8 classes, or up to 12 classes built from pasted labels.
- Accuracy, precision, recall, specificity, F1, F-beta, MCC, Cohen's kappa and balanced accuracy in one place.
- Per-class true positive, false positive, false negative and true negative counts.
- Macro, micro and weighted averages for multi-class problems, so you can see how each differs.
- Worked steps with your numbers substituted into every formula, and the formulas rendered in LaTeX.
- A ratio with a zero denominator is shown as n/a instead of a misleading 0.
- Plain-English reading of the headline metric, including a warning about accuracy on imbalanced data.
- Editable class names, so the output reads like your own problem.
Sensitivity and specificity formulas
Both are conditional on the true state.
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)Worked examples
Disease: 90 detected, 10 missed. Healthy: 30 false alarms, 170 cleared gives sensitivity 0.9, specificity 0.85
Precision (positive predictive value) is 0.75, because 30 of the 120 positive results are false alarms.
Privacy: how it works
- The matrix and labels you enter are processed by code running in your browser tab. They are not sent to Ulkaa or anyone else.
Tips
- Do not rely on accuracy alone when one class is much rarer than the others. Check recall for that class and the MCC.
- Precision and recall trade against each other. Moving a classifier's decision threshold raises one and lowers the other; the ROC and AUC calculator shows the whole trade-off.
- F1 gives equal weight to precision and recall. Use F-beta with beta above 1 when missing a positive is worse than a false alarm, and below 1 when the reverse is true.
- A metric with an empty denominator, such as precision for a class never predicted, is shown as n/a and left out of macro averages.
Frequently asked questions
Is it really free?
Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.
Do I need an account?
No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.
Does it run in my browser?
Yes. Your matrix and labels are processed by code running in your browser tab, on your own device.
Is my data uploaded?
No. Your matrix and labels are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.
What is the difference between sensitivity and specificity?
Sensitivity is the true positive rate: the share of the diseased that test positive. Specificity is the true negative rate: the share of the healthy that test negative.
Is sensitivity the same as recall?
Yes. Sensitivity, recall and true positive rate are three names for TP divided by TP plus FN.
More Confusion Matrix Calculator pages
- Confusion Matrix CalculatorEnter a confusion matrix, or paste actual and predicted labels, and get every classification metric with the working.
- Precision and Recall CalculatorEnter your confusion matrix to get precision and recall with the formula worked through.
- F1 Score CalculatorGet the F1 score of a classifier from its confusion matrix, with every step shown.
- Classification Accuracy CalculatorWork out accuracy from a confusion matrix and see when it misleads.
- MCC Calculator (Matthews Correlation Coefficient)Compute the MCC of a classifier from its confusion matrix, with the formula substituted.
- Cohen's Kappa CalculatorMeasure agreement beyond chance between two raters or between a model and the truth.
- F-beta Score CalculatorChoose beta to weight recall or precision more, and get the F-beta score with its working.
- Balanced Accuracy CalculatorAverage the recall of every class so a rare class counts as much as a common one.
- Multi-class Confusion Matrix CalculatorScore a classifier with three or more classes, class by class and on average.
- Confusion Matrix From Actual and Predicted LabelsPaste the true labels and the predictions and get the matrix and its metrics.