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特异度×召回率(灵敏度)×
领域模型评估模型评估
方法族MCDMMCDM
起源年份20th century20th century
提出者Historical statistical foundationsHistorical statistical foundations
类型Evaluation metricEvaluation metric
开创性文献Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
别名True Negative Rate, TNRSensitivity, True Positive Rate, TPR
相关55
摘要Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly.Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.
ScholarGate数据集
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  1. v1
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  3. PUBLISHED

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ScholarGate方法对比: Specificity · Recall (Sensitivity). 于 2026-06-17 检索自 https://scholargate.app/zh/compare