Comparer des méthodes

	Perte logarithmique (Entropie croisée)	Score de Brier
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1990s	1950
Auteur d'origine	Information theory and machine learning literature	Glenn W. Brier
Type	Loss function	Loss function
Source fondatrice	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
Alias	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Apparentées	3	3
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 Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.