Recall (also known as sensitivity) is the ratio of true positives (based on the confusion matrix) by all positives (=true positives + false negatives). It is commonly used in conjunction with precision and it is needed when we must minimize false negatives. Recall can be considered the opposite metric of specificity. Recall is a measure of the percentage of positive instances that are found by a machine learning model as compared to all relevant instances. In recall, we are interested in the true positive rate. Use the following reference for some good visual examples of accuracy, precision and recall:

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