Recall

Recall is a performance metric used in machine learning and statistics to evaluate the effectiveness of a classification model. It measures the proportion of true positive results in relation to the total number of actual positive instances. High recall indicates that the model is effective at identifying positive cases, making it particularly important in scenarios where missing a positive instance carries significant consequences, such as in medical diagnoses or fraud detection. Recall is often used alongside precision to provide a comprehensive view of a model's performance, especially in imbalanced datasets where one class may dominate the other.