Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Σταθμισμένη Ακρίβεια× | Ανάκληση (Ευαισθησία)× | |
|---|---|---|
| Πεδίο | Αξιολόγηση Μοντέλων | Αξιολόγηση Μοντέλων |
| Οικογένεια | MCDM | MCDM |
| Έτος προέλευσης≠ | 2010 | 20th century |
| Δημιουργός≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Τύπος | Evaluation metric | Evaluation metric |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | Average Recall, Equal-weight Average Sensitivity | Sensitivity, True Positive Rate, TPR |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. | Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|