Compara mètodes

	Puntuació de Brier	Pèrdua logarítmica (Pèrdua d'entropia creuada)
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	1950	1990s
Autor original	Glenn W. Brier	Information theory and machine learning literature
Tipus	Loss function	Loss function
Font seminal	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.
Àlies	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
Relacionats	3	3
Resum	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.