方法对比

	尤登J统计量	平衡准确率	F1分数	特异度
领域	模型评估	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM	MCDM
起源年份	1950	2010	1979	20th century
提出者	W. J. Youden	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
类型	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
开创性文献	Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32-35.	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
别名	Youden Index, Sensitivity + Specificity - 1	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	True Negative Rate, TNR
相关	3	5	5	5
摘要	Youdens J statistic, also called the Youden index, measures the maximum difference between the true positive rate and false positive rate across different classification thresholds. It is useful for selecting optimal cutoff points in diagnostic testing.	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.