Recall
Recall is a metric used in machine learning to evaluate the performance of a classification model. It measures the proportion of actual positive cases that were correctly identified by the model. In other words, it calculates the percentage of relevant instances that were retrieved by the model out of all relevant instances. Recall is particularly useful when the cost of false negatives is high, such as in medical diagnosis or fraud detection. A high recall score indicates that the model is good at identifying positive cases, but it may also produce a large number of false positives. On the other hand, a low recall score means that the model is missing many positive cases, which can be costly in certain applications.
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