Comparer des méthodes

	Exactitude	Perte logarithmique (Entropie croisée)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	20th century	1990s
Auteur d'origine	Historical statistical foundations	Information theory and machine learning literature
Type	Evaluation metric	Loss function
Source fondatrice	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.
Alias	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
Apparentées	5	3
Résumé	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.