Porovnat metody

	Přesnost	Log-Loss (křížová entropie)
Obor	Hodnocení modelů	Hodnocení modelů
Rodina	MCDM	MCDM
Rok vzniku	20th century	1990s
Tvůrce	Historical statistical foundations	Information theory and machine learning literature
Typ	Evaluation metric	Loss function
Původní zdroj	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.
Další názvy	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Příbuzné	5	3
Shrnutí	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.