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| Специфичност× | Балансирана точност× | |
|---|---|---|
| Област | Оценка на модели | Оценка на модели |
| Семейство | MCDM | MCDM |
| Година на възникване≠ | 20th century | 2010 |
| Създател≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Тип | Evaluation metric | Evaluation metric |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | True Negative Rate, TNR | Average Recall, Equal-weight Average Sensitivity |
| Свързани | 5 | 5 |
| Резюме≠ | 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. | 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 от данни ↗ |
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