Compara mètodes

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