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| Ακρίβεια (Precision)× | Συντελεστής Συσχέτισης Matthews× | |
|---|---|---|
| Πεδίο | Αξιολόγηση Μοντέλων | Αξιολόγηση Μοντέλων |
| Οικογένεια | MCDM | MCDM |
| Έτος προέλευσης≠ | 20th century | 1975 |
| Δημιουργός≠ | Historical statistical foundations | Brian W. Matthews |
| Τύπος | Evaluation metric | Evaluation metric |
| Θεμελιώδης πηγή≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | 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 ↗ |
| Εναλλακτικές ονομασίες | Positive Predictive Value, PPV | Phi Coefficient, Binary Classification Correlation |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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