Methoden vergleichen

	Log-Loss (Kreuzentropie-Verlust)	Genauigkeit	Brier-Score	F1-Score
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM	MCDM
Entstehungsjahr	1990s	20th century	1950	1979
Urheber	Information theory and machine learning literature	Historical statistical foundations	Glenn W. Brier	C. J. van Rijsbergen
Typ	Loss function	Evaluation metric	Loss function	Evaluation metric
Wegweisende Quelle	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Aliasnamen	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate	Mean Squared Probability Error	F-measure, Harmonic Mean
Verwandt	3	5	3	5
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.	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.	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.