Recall, also known as sensitivity or true positive rate, is a performance metric that measures the ability of a model to find all the relevant cases within a dataset. It is the ratio of true positive predictions to the total number of actual positives (True Positives + False Negatives). It answers the question: 'Of all the actual positive instances, how many did the model correctly identify?'
Like precision, it originated in Information Retrieval. It is also a fundamental concept in medical testing (sensitivity).
Recall is critical in high-stakes scenarios where missing a positive case is dangerous, such as cancer diagnosis (it's better to have a false alarm than to miss a tumor) or fraud detection.