Recall
A classification metric that measures the proportion of actual positive cases that a machine learning model successfully identified out of all true positives.
Think of It Like This
Like a radar system's effectiveness: out of all the enemy planes currently in the sky, what percentage did the system successfully detect?
Recall is paramount when the cost of a false negative is catastrophic. In cancer detection, missing a malignant tumor (low recall) is far worse than accidentally flagging a benign one for further review. It is the mathematical complement to Precision, and improving one almost always requires sacrificing the other.