Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Perte logarithmique (Entropie croisée)× | Score F1× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1990s | 1979 |
| Auteur d'origine≠ | Information theory and machine learning literature | C. J. van Rijsbergen |
| Type≠ | Loss function | Evaluation metric |
| Source fondatrice≠ | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. link ↗ | van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗ |
| Alias | Cross-Entropy Loss, Logloss | F-measure, Harmonic Mean |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration. | 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. |
| ScholarGateJeu de données ↗ |
|
|