Módszerek összehasonlítása

	Brier-szám	Log-Loss (Kereszt-Entrópia Vesztesség)
Tudományterület	Modellértékelés	Modellértékelés
Módszercsalád	MCDM	MCDM
Keletkezés éve	1950	1990s
Megalkotó	Glenn W. Brier	Information theory and machine learning literature
Típus	Loss function	Loss function
Alapmű	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.
Alternatív nevek	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
Kapcsolódó	3	3
Összefoglaló	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.