方法对比
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| 特异度× | 平衡准确率× | 精确率× | |
|---|---|---|---|
| 领域 | 模型评估 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM | MCDM |
| 起源年份≠ | 20th century | 2010 | 20th century |
| 提出者≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| 类型 | Evaluation metric | Evaluation metric | Evaluation metric |
| 开创性文献≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | 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. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| 别名 | True Negative Rate, TNR | Average Recall, Equal-weight Average Sensitivity | Positive Predictive Value, PPV |
| 相关 | 5 | 5 | 5 |
| 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. | 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. | Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly. |
| ScholarGate数据集 ↗ |
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