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