In the context of a classification model, what does sensitivity measure?

Sensitivity, also known as true positive rate or recall, measures the ability of a classification model to correctly identify positive instances out of all actual positive instances. This means it specifically focuses on the proportion of true positives—those cases where the model correctly predicts a positive outcome—compared to the total number of actual positives.

By calculating sensitivity, you can assess how well the model is functioning specifically for the class of interest, particularly in situations where identifying positive cases is critical, such as in medical diagnoses or fraud detection. A high sensitivity indicates that the model is effective at detecting cases that are genuinely positive, which is vital for avoiding missed cases (false negatives) that could have significant implications depending on the context.

The other choices address different aspects of the model's performance and do not align with the definition of sensitivity. For example, the total number of classified instances does not provide insight into the model's effectiveness at identifying true positives. Similarly, accuracy of negative predictions and overall error rate pertain to different metrics that evaluate the performance of the model without specifically focusing on how well it identifies true positives.