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