Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usawa wa Usahihi (Balanced Accuracy)× | Usahihi× | |
|---|---|---|
| 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 | Positive Predictive Value, PPV |
| 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. | Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly. |
| ScholarGateSeti ya data ↗ |
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