Comparer des méthodes

	Score de Brier	Perte logarithmique (Entropie croisée)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1950	1990s
Auteur d'origine	Glenn W. Brier	Information theory and machine learning literature
Type	Loss function	Loss function
Source fondatrice	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Alias	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
Apparentées	3	3
Résumé	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.	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.