Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usawa wa Usahihi (Balanced Accuracy)× | Umahiri (Specificity)× | |
|---|---|---|
| Nyanja | Tathmini ya Modeli | Tathmini ya Modeli |
| Familia | MCDM | MCDM |
| Mwaka wa asili≠ | 2010 | 20th century |
| Mwanzilishi≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Aina | Evaluation metric | Evaluation metric |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Average Recall, Equal-weight Average Sensitivity | True Negative Rate, TNR |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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