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 | Overall Accuracy, Correct Classification Rate |
| 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. | 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. |
| ScholarGateSeti ya data ↗ |
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