Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полнота (Чувствительность)× | Сбалансированная точность× | |
|---|---|---|
| Область | Оценка моделей | Оценка моделей |
| Семейство | MCDM | MCDM |
| Год появления≠ | 20th century | 2010 |
| Автор метода≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Тип | Evaluation metric | Evaluation metric |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | Sensitivity, True Positive Rate, TPR | Average Recall, Equal-weight Average Sensitivity |
| Связанные | 5 | 5 |
| Сводка≠ | 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 данных ↗ |
|
|