What does recall help to determine in predictive modeling?

Recall is a crucial metric in predictive modeling, particularly in the context of classification problems where identifying positive instances is key. It specifically measures the ability of the model to correctly identify all relevant instances within a dataset. In simpler terms, recall quantifies the proportion of actual positive cases that were correctly predicted by the model. This metric is particularly important in scenarios where the consequences of missing a positive instance (a false negative) are significant.

By focusing on the completeness of positive predictions, recall ensures that even if a model has a high overall predictive accuracy, it can still be failing to identify many of the positive instances, leading to a potentially misleading overall effectiveness. Recall does not account for true negatives or provide information on the total accuracy of the model; instead, it zooms in specifically on the model's performance related to the positive class.

Understanding recall helps analysts and data scientists assess whether a model is appropriately focused on capturing the relevant instances that matter most, especially in fields like healthcare or fraud detection, where failing to identify a positive case can have serious consequences.