Σύγκριση μεθόδων

	Απώλεια λογαρίθμου (Απώλεια διασταυρούμενης εντροπίας)	Βαθμολογία Brier
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM
Έτος προέλευσης	1990s	1950
Δημιουργός	Information theory and machine learning literature	Glenn W. Brier
Τύπος	Loss function	Loss function
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
Συναφείς	3	3
Σύνοψη	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.