Methoden vergleichen

	Brier-Score	Log-Loss (Kreuzentropie-Verlust)	Mittlerer Absoluter Fehler (MAE)
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM
Entstehungsjahr	1950	1990s	1799
Urheber	Glenn W. Brier	Information theory and machine learning literature	Pierre-Simon Laplace
Typ	Loss function	Loss function	Robust distance-based metric
Wegweisende Quelle	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.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
Aliasnamen	Mean Squared Probability Error	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
Verwandt	3	3	3
Zusammenfassung	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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.