Salīdzināt metodes

	Log-Loss (krustentropijas zudums)	Precizitāte
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1990s	20th century
Autors	Information theory and machine learning literature	Historical statistical foundations
Tips	Loss function	Evaluation metric
Pirmavots	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.
Citi nosaukumi	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Saistītās	3	5
Kopsavilkums	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.