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| Độ chính xác cân bằng× | Độ chính xác× | Điểm F1× | |
|---|---|---|---|
| Lĩnh vực | Đánh giá mô hình | Đánh giá mô hình | Đánh giá mô hình |
| Họ | MCDM | MCDM | MCDM |
| Năm ra đời≠ | 2010 | 20th century | 1979 |
| Người khởi xướng≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations | C. J. van Rijsbergen |
| Loại | Evaluation metric | Evaluation metric | Evaluation metric |
| Công trình gốc≠ | 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 ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| Tên gọi khác | Average Recall, Equal-weight Average Sensitivity | Overall Accuracy, Correct Classification Rate | F-measure, Harmonic Mean |
| Liên quan | 5 | 5 | 5 |
| Tóm tắt≠ | 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. | The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important. |
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