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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Précision équilibrée× | Rappel (Sensibilité)× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 2010 | 20th century |
| Auteur d'origine≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Type | Evaluation metric | Evaluation metric |
| Source fondatrice≠ | 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 ↗ |
| Alias≠ | Average Recall, Equal-weight Average Sensitivity | Sensitivity, True Positive Rate, TPR |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly. |
| ScholarGateJeu de données ↗ |
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