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	Brierov rezultat	Log-Loss (Gubitak logaritma / Križna entropija)
Područje	Evaluacija modela	Evaluacija modela
Obitelj	MCDM	MCDM
Godina nastanka	1950	1990s
Tvorac	Glenn W. Brier	Information theory and machine learning literature
Vrsta	Loss function	Loss function
Temeljni izvor	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.
Drugi nazivi	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
Srodne	3	3
Sažetak	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.