What does recall indicate in a classification model?

Recall is a crucial metric in evaluating the performance of a classification model, particularly in contexts where the positive class is of higher interest, such as in medical diagnosis or fraud detection. Specifically, recall measures the model's ability to identify true positives among all actual positive instances. In other words, it indicates how many of the actual positive cases were correctly predicted by the model.

When we say that recall reflects the model's ability to avoid false negatives, we highlight that it focuses on the instances where the model failed to predict a positive outcome when it was, in fact, positive. A high recall value means that the model is effectively capturing a large portion of the actual positive instances, thereby minimizing the false negative rate. This is particularly vital in situations where missing a positive case could lead to significant consequences.

The other choices do not pertain to what recall measures: the total number of predictions made reflects the overall volume of outputs, the accuracy of negative predictions pertains to how well the model is identifying negative cases, and the number of features in a dataset relates to the complexity of the model rather than its classification performance. Recall focuses specifically on the positive class predictions and their connection to actual occurrences, making it a critical performance metric for classification scenarios where detecting positives is essential.