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| Σταθμισμένη Ακρίβεια× | Ειδικότητα× | |
|---|---|---|
| Πεδίο | Αξιολόγηση Μοντέλων | Αξιολόγηση Μοντέλων |
| Οικογένεια | MCDM | MCDM |
| Έτος προέλευσης≠ | 2010 | 20th century |
| Δημιουργός≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Τύπος | Evaluation metric | Evaluation metric |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες | Average Recall, Equal-weight Average Sensitivity | True Negative Rate, TNR |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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