Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сбалансированная точность× | Точность× | |
|---|---|---|
| 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 | Positive Predictive Value, PPV |
| 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. | Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly. |
| ScholarGateНабор данных ↗ |
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