Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Score F1× | Perte de Hamming× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1979 | 2000s |
| Auteur d'origine≠ | C. J. van Rijsbergen | Information theory and multi-label learning |
| Type≠ | Evaluation metric | Loss function |
| Source fondatrice≠ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ | Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168. DOI ↗ |
| Alias | F-measure, Harmonic Mean | Hamming Distance, Subset Accuracy Loss |
| Apparentées≠ | 5 | 1 |
| Résumé≠ | 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. | Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems. |
| ScholarGateJeu de données ↗ |
|
|