Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сбалансированная точность× | Полнота (Чувствительность)× | |
|---|---|---|
| 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 | Sensitivity, True Positive Rate, TPR |
| 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. | 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 данных ↗ |
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