Recall is a performance metric used in machine learning to measure how often a model correctly identifies positive instances from all actual positive samples in a dataset. It is also known as the true positive rate (TPR) . Recall is calculated by dividing the number of true positives (TP) by the sum of true positives and false negatives (FN) . In other words, recall answers the question: can an ML model find all instances of the positive class? .
Recall is a metric used for classification in supervised learning, and it is particularly useful when the cost of false negatives is high. For example, in medical diagnosis, a false negative could mean a missed diagnosis, which could have serious consequences for the patient.
Recall is related to precision, another performance metric used in machine learning, but they measure different things. Precision measures the proportion of true positives among all predicted positives, while recall measures the proportion of true positives among all actual positives.
Recall can be improved by increasing the number of true positives and decreasing the number of false negatives. However, increasing recall often comes at the cost of decreasing precision, as the model may start to classify more false positives as true positives.
