MCDMClassification Metric
特异度
特异度衡量的是分类器正确识别为阴性的实际阴性病例的比例。它回答了这样一个问题：“在所有真正为阴性的病例中，我们正确拒绝了多少？”特异度与召回率互补，当假阳性代价高昂时至关重要。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI: 10.1016/j.patrec.2005.10.010 ↗
- Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63. link ↗
如何引用本页
ScholarGate. (2026, June 3). Specificity (True Negative Rate). ScholarGate. https://scholargate.app/zh/model-evaluation/specificity
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 平衡准确率模型评估↔ compare
- F1分数模型评估↔ compare
- Matthews Correlation Coefficient模型评估↔ compare
- 精确率模型评估↔ compare
- 召回率（灵敏度）模型评估↔ compare