Recall

Recall in machine learning is a metric used in classification tasks. It measures the proportion of actual positives correctly identified by the model (True Positives) out of all actual positives (True Positives + False Negatives).

Importance of Recall

Recall is crucial when the cost of missing a positive instance is high. For example, in medical diagnosis, a high recall rate ensures that most positive cases are correctly identified, even if some negative cases are incorrectly labeled as positive.