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| Hệ số Tương quan Matthews× | Độ chính xác cân bằng× | Độ chính xác (Precision)× | |
|---|---|---|---|
| Lĩnh vực | Đánh giá mô hình | Đánh giá mô hình | Đánh giá mô hình |
| Họ | MCDM | MCDM | MCDM |
| Năm ra đời≠ | 1975 | 2010 | 20th century |
| Người khởi xướng≠ | Brian W. Matthews | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Loại | Evaluation metric | Evaluation metric | Evaluation metric |
| Công trình gốc≠ | Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451. 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 ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Tên gọi khác | Phi Coefficient, Binary Classification Correlation | Average Recall, Equal-weight Average Sensitivity | Positive Predictive Value, PPV |
| Liên quan | 5 | 5 | 5 |
| Tóm tắt≠ | The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets. | 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. |
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