विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्मरण (संवेदनशीलता)× | संतुलित सटीकता (Balanced Accuracy)× | |
|---|---|---|
| क्षेत्र | मॉडल मूल्यांकन | मॉडल मूल्यांकन |
| परिवार | MCDM | MCDM |
| उद्भव वर्ष≠ | 20th century | 2010 |
| प्रवर्तक≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann |
| प्रकार | Evaluation metric | Evaluation metric |
| मौलिक स्रोत≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | 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 ↗ |
| उपनाम≠ | Sensitivity, True Positive Rate, TPR | Average Recall, Equal-weight Average Sensitivity |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. | 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. |
| ScholarGateडेटासेट ↗ |
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