Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сбалансированная точность× | Специфичность× | |
|---|---|---|
| Область | Оценка моделей | Оценка моделей |
| Семейство | MCDM | MCDM |
| Год появления≠ | 2010 | 20th century |
| Автор метода≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Тип | Evaluation metric | Evaluation metric |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | Average Recall, Equal-weight Average Sensitivity | True Negative Rate, TNR |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly. |
| ScholarGateНабор данных ↗ |
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