Usporedite metode

	Log-Loss (Gubitak logaritma / Križna entropija)	Točnost
Područje	Evaluacija modela	Evaluacija modela
Obitelj	MCDM	MCDM
Godina nastanka	1990s	20th century
Tvorac	Information theory and machine learning literature	Historical statistical foundations
Vrsta	Loss function	Evaluation metric
Temeljni izvor	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.
Drugi nazivi	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Srodne	3	5
Sažetak	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.