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	Log-Loss (Entropia Incrociata)	Punteggio di Brier
Campo	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM
Anno di origine	1990s	1950
Ideatore	Information theory and machine learning literature	Glenn W. Brier
Tipo	Loss function	Loss function
Fonte seminale	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
Correlati	3	3
Sintesi	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.