Precision-Recall Curve
A Precision-Recall Curve (PR Curve) is a graphical plot used in machine learning and statistics to evaluate the performance of binary classification models, particularly in imbalanced datasets. It illustrates the trade-off between precision (the proportion of true positives among predicted positives) and recall (the proportion of true positives among actual positives) across different classification thresholds. The area under the PR curve (AUC-PR) serves as a summary metric, with higher values indicating better model performance.
Developers should learn and use Precision-Recall Curves when working with binary classification tasks where classes are imbalanced, such as fraud detection, medical diagnosis, or spam filtering, as they provide a more informative assessment than ROC curves in such scenarios. They are essential for optimizing models to balance false positives and false negatives, helping to select appropriate thresholds based on specific business or application needs, like prioritizing high recall in safety-critical systems.