Σύγκριση μεθόδων

	Απώλεια λογαρίθμου (Απώλεια διασταυρούμενης εντροπίας)	Ακρίβεια
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM
Έτος προέλευσης	1990s	20th century
Δημιουργός	Information theory and machine learning literature	Historical statistical foundations
Τύπος	Loss function	Evaluation metric
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Συναφείς	3	5
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.	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.