قارن الطرق

	خسارة اللوغاريتم (خسارة الإنتروبيا المتقاطعة)	متوسط الخطأ المطلق (MAE)
المجال	تقييم النماذج	تقييم النماذج
العائلة	MCDM	MCDM
سنة النشأة	1990s	1799
صاحب الطريقة	Information theory and machine learning literature	Pierre-Simon Laplace
النوع	Loss function	Robust distance-based metric
المصدر التأسيسي	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
الأسماء البديلة	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
ذات صلة	3	3
الملخص	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.