Methoden vergleichen

	Log-Loss (Kreuzentropie-Verlust)	Brier-Score
Fachgebiet	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM
Entstehungsjahr	1990s	1950
Urheber	Information theory and machine learning literature	Glenn W. Brier
Typ	Loss function	Loss function
Wegweisende Quelle	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.
Aliasnamen	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Verwandt	3	3
Zusammenfassung	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.