Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| F1-score× | Precisie× | Gevoeligheid (Recall)× | |
|---|---|---|---|
| Vakgebied | Modelevaluatie | Modelevaluatie | Modelevaluatie |
| Familie | MCDM | MCDM | MCDM |
| Jaar van ontstaan≠ | 1979 | 20th century | 20th century |
| Grondlegger≠ | C. J. van Rijsbergen | Historical statistical foundations | Historical statistical foundations |
| Type | Evaluation metric | Evaluation metric | Evaluation metric |
| Oorspronkelijke bron≠ | 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 ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Aliassen≠ | F-measure, Harmonic Mean | Positive Predictive Value, PPV | Sensitivity, True Positive Rate, TPR |
| Verwant | 5 | 5 | 5 |
| Samenvatting≠ | 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. | 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. | 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. |
| ScholarGateGegevensset ↗ |
|
|
|