مقایسهٔ روش‌ها

	زیان لگاریتمی (زیان آنتروپی متقاطع)	دقت
حوزه	ارزیابی مدل	ارزیابی مدل
خانواده	MCDM	MCDM
سال پیدایش	1990s	20th century
پدیدآور	Information theory and machine learning literature	Historical statistical foundations
نوع	Loss function	Evaluation metric
منبع بنیادین	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.
نام‌های دیگر	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
مرتبط	3	5
خلاصه	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.