Uporedite metode

	Log-Loss (Gubitak po logaritmu / unakrsna entropija)	Brjerova mera (Brier Score)
Oblast	Evaluacija modela	Evaluacija modela
Porodica	MCDM	MCDM
Godina nastanka	1990s	1950
Tvorac	Information theory and machine learning literature	Glenn W. Brier
Tip	Loss function	Loss function
Temeljni izvor	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.
Drugi nazivi	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Srodne	3	3
Sažetak	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.