Which equation represents Recall in the context of a Confusion Matrix?

The concept of Recall in the context of a Confusion Matrix specifically measures the ability of a model to identify all relevant instances within a dataset, typically focusing on the positive instances. Recall is calculated as the ratio of True Positives to the total number of actual positives, which effectively captures how many of the positive cases were correctly identified by the model.

In this case, the equation for Recall is represented as True Positives divided by the sum of True Positives and False Negatives. This measurement provides insights into the model's effectiveness at recognizing positive cases, revealing its capacity to avoid overlooking relevant instances.

The correct choice underscores the importance of True Positives in the formula, emphasizing that Recall assesses the model's success specifically in capturing positive outcomes. This showcases Recall's role as a crucial metric, especially in scenarios where missing a positive instance carries significant consequences, such as in medical diagnoses where failing to detect a disease could lead to severe outcomes.