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When positive and negative samples are imbalanced, the Precision-Recall (PR) Curve is more informative than the ROC curve; since harmful samples in guardrail testing are usually far fewer than normal samples, the PR curve is generally more appropriate.
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8/30/2026
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UnverifiedWhen class imbalance is severe, the PR curve reflects the model's true performance better than the ROC curve.79% similarUnverified当正负样本比例发生变化时，ROC曲线的形状能够基本保持不变，而PR曲线通常会出现较大波动74% similarUnverifiedIn highly imbalanced datasets (with a positive rate below 5%), the area under the precision-recall curve (AP) often reflects the model's ability to detect the minority class better than AUC does.70% similarUnverifiedThe ROC curve uses the false positive rate FPR = FP / (FP + TN) as the horizontal axis; when the number of negative samples is extremely large, the FPR will appear very low.68% similarUnverifiedWhen the ratio of positive to negative samples is extreme, the area under the PR curve (AUPRC) is often more revealing of true performance than AUC.67% similar
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