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| Exactitud× | Recordació (Sensibilitat)× | |
|---|---|---|
| Camp | Avaluació de models | Avaluació de models |
| Família | MCDM | MCDM |
| Any d'origen | 20th century | 20th century |
| Autor original | Historical statistical foundations | Historical statistical foundations |
| Tipus | Evaluation metric | Evaluation metric |
| Font seminal | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Àlies≠ | Overall Accuracy, Correct Classification Rate | Sensitivity, True Positive Rate, TPR |
| Relacionats | 5 | 5 |
| Resum≠ | 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. | 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. |
| ScholarGateConjunt de dades ↗ |
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