Methoden vergelijken

	Log-verlies (Cross-Entropy Loss)	Brier Score
Vakgebied	Modelevaluatie	Modelevaluatie
Familie	MCDM	MCDM
Jaar van ontstaan	1990s	1950
Grondlegger	Information theory and machine learning literature	Glenn W. Brier
Type	Loss function	Loss function
Oorspronkelijke bron	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.
Aliassen	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Verwant	3	3
Samenvatting	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.