recall, or sensitivity, as a classification metric, is the proportion of correctly predicted observations in one class out of all observations in that class. Or the ratio of TRUE positives out of all ACTUAL positives
This has a formula opposite of specificity
Transclude of specificity
Usage
recall is important when we believe False Negatives are more important than False Positives (e.g. problem of cancer detection).
- Out of survived passengers, how many did we label correctly?
- Out of the sick patients, how many did we correctly diagnose as sick?