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| Specificitás× | Kiegyensúlyozott pontosság× | |
|---|---|---|
| Tudományterület | Modellértékelés | Modellértékelés |
| Módszercsalád | MCDM | MCDM |
| Keletkezés éve≠ | 20th century | 2010 |
| Megalkotó≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Típus | Evaluation metric | Evaluation metric |
| Alapmű≠ | 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 ↗ |
| Alternatív nevek | True Negative Rate, TNR | Average Recall, Equal-weight Average Sensitivity |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. |
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