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