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