Recall

The proportion of actual positive cases that the model correctly identifies. High recall means the model catches most of the true positives, but may do so at the cost of many false alarms. Recall is most important in applications where missing a true positive is costly, such as fraud detection or disease screening.
See also: precision, f1 score.