قارن الطرق

	الدقة	خسارة اللوغاريتم (خسارة الإنتروبيا المتقاطعة)
المجال	تقييم النماذج	تقييم النماذج
العائلة	MCDM	MCDM
سنة النشأة	20th century	1990s
صاحب الطريقة	Historical statistical foundations	Information theory and machine learning literature
النوع	Evaluation metric	Loss function
المصدر التأسيسي	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.
الأسماء البديلة	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss
ذات صلة	5	3
الملخص	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.