Jämför metoder
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| Recall (känslighet)× | Balanserad träffsäkerhet× | Precision× | |
|---|---|---|---|
| Ämnesområde | Modellutvärdering | Modellutvärdering | Modellutvärdering |
| Familj | MCDM | MCDM | MCDM |
| Ursprungsår≠ | 20th century | 2010 | 20th century |
| Upphovsperson≠ | Historical statistical foundations | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Typ | Evaluation metric | Evaluation metric | Evaluation metric |
| Ursprungskälla≠ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. 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 ↗ |
| Alias≠ | Sensitivity, True Positive Rate, TPR | Average Recall, Equal-weight Average Sensitivity | Positive Predictive Value, PPV |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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. | 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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