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