השוואת שיטות

	Log-Loss (Cross-Entropy Loss)	ציון ברייר
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1990s	1950
הוגה השיטה	Information theory and machine learning literature	Glenn W. Brier
סוג	Loss function	Loss function
מקור מכונן	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
כינויים	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
קשורות	3	3
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.	The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.