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| Recall (Sensitivität)× | Matthews-Korrelationskoeffizient× | |
|---|---|---|
| Fachgebiet | Modellevaluation | Modellevaluation |
| Familie | MCDM | MCDM |
| Entstehungsjahr≠ | 20th century | 1975 |
| Urheber≠ | Historical statistical foundations | Brian W. Matthews |
| Typ | Evaluation metric | Evaluation metric |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | Sensitivity, True Positive Rate, TPR | Phi Coefficient, Binary Classification Correlation |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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. |
| ScholarGateDatensatz ↗ |
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