PRC (Precision-Recall Curve) analysis is a crucial technique for measuring the performance of classification models. It provides a comprehensive understanding of how the model's precision and recall fluctuate across different threshold points. By plotting the precision-recall pairs, we can identify the optimal point that balances these two metrics