Recall

Recall, in the context of machine learning, is a metric used to measure the ability of a model to identify all relevant instances within a dataset. It is calculated as the ratio of true positives (correctly identified positives) to the sum of true positives and false negatives (positives the model failed to identify). This metric is particularly important in scenarios where missing a positive instance has serious implications, such as in medical diagnosis or fraud detection.