Võrdle meetodeid

	Log-Loss (Rist-entroopia kaotus)	Briieri skoor
Valdkond	Mudelite hindamine	Mudelite hindamine
Perekond	MCDM	MCDM
Tekkeaasta	1990s	1950
Looja	Information theory and machine learning literature	Glenn W. Brier
Tüüp	Loss function	Loss function
Algallikas	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.
Rööpnimetused	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Seotud	3	3
Kokkuvõte	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.