Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Přesnost× | Vyvážená přesnost× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 20th century | 2010 |
| Tvůrce≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| Typ | Evaluation metric | Evaluation metric |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | Overall Accuracy, Correct Classification Rate | Average Recall, Equal-weight Average Sensitivity |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | 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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