Precision-Recall Curve

A Precision-Recall (PR) Curve is a graph that helps evaluate the performance of a classification model, especially when dealing with imbalanced data. It plots two metrics: Precision (the accuracy of positive predictions) and Recall (the ability to detect all positive cases). By changing the threshold for deciding if a prediction is positive, the curve shows the trade-off between precision and recall. A higher area under the curve indicates a better model. PR Curves are particularly useful when the focus is on correctly identifying positive instances, such as in medical diagnoses or fraud detection.