השוואת שיטות

	Log-Loss (Cross-Entropy Loss)	דיוק
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1990s	20th century
הוגה השיטה	Information theory and machine learning literature	Historical statistical foundations
סוג	Loss function	Evaluation metric
מקור מכונן	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.
כינויים	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
קשורות	3	5
תקציר	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.