Compară metode

	Log-Loss (Pierdere de Entropie Încrucișată)	Scorul Brier
Domeniu	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM
Anul apariției	1990s	1950
Autorul original	Information theory and machine learning literature	Glenn W. Brier
Tip	Loss function	Loss function
Sursa seminală	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.
Denumiri alternative	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Înrudite	3	3
Rezumat	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.