Methoden vergleichen

	Genauigkeit	Log-Loss (Kreuzentropie-Verlust)	Mittlerer Absoluter Fehler (MAE)
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM
Entstehungsjahr	20th century	1990s	1799
Urheber	Historical statistical foundations	Information theory and machine learning literature	Pierre-Simon Laplace
Typ	Evaluation metric	Loss function	Robust distance-based metric
Wegweisende Quelle	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
Verwandt	5	3	3
Zusammenfassung	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.