مقایسهٔ روش‌ها

	زیان لگاریتمی (زیان آنتروپی متقاطع)	میانگین خطای مطلق (MAE)
حوزه	ارزیابی مدل	ارزیابی مدل
خانواده	MCDM	MCDM
سال پیدایش	1990s	1799
پدیدآور	Information theory and machine learning literature	Pierre-Simon Laplace
نوع	Loss function	Robust distance-based metric
منبع بنیادین	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
نام‌های دیگر	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
مرتبط	3	3
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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.