方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 平衡准确率× | 精确率× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 2010 | 20th century |
| 提出者≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| 类型 | Evaluation metric | Evaluation metric |
| 开创性文献≠ | 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 | Average Recall, Equal-weight Average Sensitivity | Positive Predictive Value, PPV |
| 相关 | 5 | 5 |
| 摘要≠ | 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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