Sammenlign metoder

	Log-tap (kryssentropitap)	Nøyaktighet
Fagfelt	Modellevaluering	Modellevaluering
Familie	MCDM	MCDM
Opprinnelsesår	1990s	20th century
Opphavsperson	Information theory and machine learning literature	Historical statistical foundations
Type	Loss function	Evaluation metric
Opprinnelig kilde	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.
Alias	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Relaterte	3	5
Sammendrag	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.