Comparer des méthodes

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