Salīdzināt metodes

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