Usporedite metode

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