방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 균형 정확도× | 정밀도(Precision)× | |
|---|---|---|
| 분야 | 모델 평가 | 모델 평가 |
| 계열 | MCDM | MCDM |
| 기원 연도≠ | 2010 | 20th century |
| 창시자≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| 유형 | Evaluation metric | Evaluation metric |
| 원전≠ | 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 ↗ |
| 별칭 | Average Recall, Equal-weight Average Sensitivity | Positive Predictive Value, PPV |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|