Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Точность× | 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 | Overall Accuracy, Correct Classification Rate | Average Recall, Equal-weight Average Sensitivity |
| Связанные | 5 | 5 |
| Сводка≠ | Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class. | 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 ↗ |
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