方法对比
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| 平衡准确率× | 混淆矩阵× | |
|---|---|---|
| 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 | Statistical foundations |
| 类型≠ | Evaluation metric | Evaluation visualization |
| 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 ↗ | Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC. link ↗ |
| 别名 | Average Recall, Equal-weight Average Sensitivity | Error Matrix, Contingency Table |
| 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. | The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics. |
| ScholarGate数据集 ↗ |
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