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| Покритие (Чувствителност)× | Балансирана точност× | |
|---|---|---|
| Област | Оценка на модели | Оценка на модели |
| Семейство | MCDM | MCDM |
| Година на възникване≠ | 20th century | 2010 |
| Създател≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Тип | Evaluation metric | Evaluation metric |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | Sensitivity, True Positive Rate, TPR | Average Recall, Equal-weight Average Sensitivity |
| Свързани | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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