Recall
Recall is a classification metric that measures the proportion of actual positive cases that a model correctly identifies. It indicates how effectively a model detects positive instances without missing them.
What is Recall?
Recall is calculated as the number of true positives divided by the total number of actual positive cases, including both true positives and false negatives. A high recall means the model identifies most of the positive cases in the dataset.
Why is Recall Important?
Recall is particularly important when missing a positive case has significant consequences. It helps teams understand how well a model detects relevant instances and is often evaluated alongside precision to assess classification performance.
Common use cases
Recall is commonly used in fraud detection, medical diagnosis, threat detection, spam filtering, content moderation, and information retrieval.