Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Tasapainotettu tarkkuus× | Tarkkuus× | |
|---|---|---|
| Tieteenala | Mallien arviointi | Mallien arviointi |
| Menetelmäperhe | MCDM | MCDM |
| Syntyvuosi≠ | 2010 | 20th century |
| Kehittäjä≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Tyyppi | Evaluation metric | Evaluation metric |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | Average Recall, Equal-weight Average Sensitivity | Overall Accuracy, Correct Classification Rate |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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