What is Recall?
Recall, also known as sensitivity, is an essential metric for evaluating the performance of a classification model. It is the ratio of correctly predicted positive observations to all actual positives. It provides insights into the ability of a model to capture positive cases.
In mathematical terms:
Recall = True Positives / (True Positives + False Negatives)
Importance of Recall
The recall is crucial when the cost of false negatives is high, such as in medical diagnoses or fraud detection. A high recall indicates fewer positive samples are missed by the model, which is highly desirable in sensitive applications.
Learn more about these related metrics: