Compară metode

	Acuratețe	Log-Loss (Pierdere de Entropie Încrucișată)
Domeniu	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM
Anul apariției	20th century	1990s
Autorul original	Historical statistical foundations	Information theory and machine learning literature
Tip	Evaluation metric	Loss function
Sursa seminală	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.
Denumiri alternative	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Înrudite	5	3
Rezumat	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.