Porovnat metody

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