A precision-recall curve is a graphical representation that illustrates the trade-off between precision and recall for different threshold values in a classification model. It helps evaluate the performance of a model, especially when dealing with imbalanced datasets, by showing how well the model can identify positive instances while minimizing false positives. The curve is especially relevant in anomaly detection, as it provides insight into the model's effectiveness at detecting rare events.
congrats on reading the definition of Precision-Recall Curve. now let's actually learn it.