Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vyvážená přesnost× | Přesnost× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 2010 | 20th century |
| Tvůrce≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Typ | Evaluation metric | Evaluation metric |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | Average Recall, Equal-weight Average Sensitivity | Overall Accuracy, Correct Classification Rate |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class. |
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