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| Recall (感度)× | Balanced Accuracy× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年≠ | 20th century | 2010 |
| 提唱者≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| 種類 | Evaluation metric | Evaluation metric |
| 原典≠ | 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 ↗ |
| 別名≠ | Sensitivity, True Positive Rate, TPR | Average Recall, Equal-weight Average Sensitivity |
| 関連 | 5 | 5 |
| 概要≠ | Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is 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. |
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