To evaluate the performance of my medical diagnostic system, I would calculate precision and recall metrics on the dataset. Precision is the fraction of true positives among all detected cases, while recall is the fraction of true events that were detected. A high precision means few false positives, but may miss some actual cases (low recall). A high recall means most actual cases are detected, but may also include many false positives. By plotting a PR curve with precision on the y-axis and recall on the x-axis, I can visualize the tradeoff between precision and recall. For example, if my model has perfect recall but low precision, it would detect almost all true cases, but also incorrectly classify many healthy patients as diseased.
