When assessing model performance, which of the following will give the best understanding of the model’s ability to capture relevant instances?

The best understanding of a model’s ability to capture relevant instances is provided by recall. Recall, also known as sensitivity or true positive rate, measures the ratio of true positives to the total number of actual positives. This metric highlights how well the model can identify all relevant instances within the dataset. In scenarios where identifying all relevant instances is critical, such as in medical diagnosis or fraud detection, recall is particularly valuable.

While other metrics like precision, accuracy, and specificity provide important insights into model performance, they focus on different aspects. Precision evaluates the correctness of positive predictions, accuracy measures the overall correctness of the model, and specificity looks at how well the model identifies negatives. However, none of these metrics alone reflects the model's ability to capture all relevant instances as comprehensively as recall does, making it the most relevant measure for this context.