What does the term sensitivity refer to in model assessment?

The term sensitivity, often referred to as the true positive rate, specifically measures the ability of a model to correctly identify positive cases. It is calculated as the ratio of true positives to the sum of true positives and false negatives. This definition aligns with the understanding that sensitivity assesses the effectiveness of a model in recognizing positive outcomes among those that are indeed positive.

In the context of model assessment, sensitivity is crucial, especially in scenarios where detecting positive cases is a priority, such as in medical diagnoses or fraud detection. High sensitivity indicates that the model successfully identifies most of the positive cases, minimizing the risk of false negatives.

While some options mention relevant aspects of model assessment, such as class distinction and case identification, they do not accurately define sensitivity as it directly relates to the proportion of true positives identified against those that are missed. This precision is what makes the concept of sensitivity an essential metric in evaluating a model's performance, particularly in imbalanced datasets where identifying the minority class is critical.