Unverified60% confidenceFactExact time
The false positive rate is calculated as FPR = FP / (FP + TN) and is one of the core metrics for measuring classifier quality.
2
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60%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Related Claims
UnverifiedThe 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.79% similarUnverifiedThe false positive rate (FPR) uses all negative samples as its denominator, so when the number of negatives is huge, a seemingly small FPR still produces a large number of false alarms (for example, an FPR of 0.05 over 9,900 negatives corresponds to 495 false alarms).75% similarUnverifiedThe false discovery rate (FDR) is a statistical metric in multiple hypothesis testing that controls the proportion of false rejections, defined as the expected value of the proportion of rejected null hypotheses that are actually true.70% similarUnverifiedTPR@1% FPR denotes the highest detection rate achievable when the false positive rate is strictly controlled at 1%.70% similarUnverified评估基准测试质量的关键指标是假阳性率和假阴性率，理想基准应将两者控制在较低水平66% similar
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