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| Koefisien Korelasi Matthews× | Skor F1× | Deria (Sensitiviti)× | |
|---|---|---|---|
| Bidang | Penilaian Model | Penilaian Model | Penilaian Model |
| Keluarga | MCDM | MCDM | MCDM |
| Tahun asal≠ | 1975 | 1979 | 20th century |
| Pengasas≠ | Brian W. Matthews | C. J. van Rijsbergen | Historical statistical foundations |
| Jenis | Evaluation metric | Evaluation metric | Evaluation metric |
| Sumber perintis≠ | 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 ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Alias≠ | Phi Coefficient, Binary Classification Correlation | F-measure, Harmonic Mean | Sensitivity, True Positive Rate, TPR |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | 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. | 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. | 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. |
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