difficulty 2/5
Specificity
Of everything that really was negative, the fraction the model correctly left alone — recall measured on the negative class.
Intuition
Recall asks how many of the real positives the model caught. Specificity asks the same question about the other class: of everything that was genuinely negative, how much did the model correctly leave alone?
It is the metric that notices false alarms from the perspective of the healthy population rather than the flagged queue. That distinction matters more than it sounds, and it is the reason medicine reports specificity where machine learning usually reports precision.
Formal definition
How it works
Where it's used
Why & when
Worked example
Pitfalls
Check yourself
3 questions
The questions on this page come with access. They are graded on the server, so the answers stay there until you have given yours.
On the cheat sheet: Classification metrics →
Sources
- scikit-learn — sklearn.metrics.confusion_matrixdocs · retrieved 2026-08-30https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
- Machine Learning Crash Course — Classification: accuracy, recall, precision, and related metricscourse · retrieved 2026-10-04https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall
- Machine Learning Crash Course — Classification: ROC and AUCcourse · retrieved 2026-10-06https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
- Introduction to Information Retrieval (2008)bookhttps://nlp.stanford.edu/IR-book/html/htmledition/irbook.html