Σύγκριση μεθόδων

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