השוואת שיטות

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