difficulty 1/5
Recall
Of everything that really was positive, the fraction the model actually found — the measure of how much it misses.
Intuition
There were a hundred real cases out there. How many did the model catch?
Recall counts only the cases that were genuinely positive and asks what fraction the model found. False alarms are irrelevant to it — a model that flags everything scores perfect recall [2, Recall, or true positive rate]. That is the mirror image of precision, and the reason the two are always read together.
The metric goes by three names depending on the field: recall in information retrieval, sensitivity in medicine, true positive rate when drawing ROC curves. They are the same number [4, §3.4.4.9].
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.recall_scoredocs · retrieved 2026-08-30https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.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 — Thresholds and the confusion matrixcourse · retrieved 2026-10-04https://developers.google.com/machine-learning/crash-course/classification/thresholding
- scikit-learn User Guide — Metrics and scoringdocs · retrieved 2026-10-04https://scikit-learn.org/stable/modules/model_evaluation.html
- Introduction to Information Retrieval (2008)bookhttps://nlp.stanford.edu/IR-book/html/htmledition/irbook.html