השוואת שיטות

	ציון ברייר	Log-Loss (Cross-Entropy Loss)
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1950	1990s
הוגה השיטה	Glenn W. Brier	Information theory and machine learning literature
סוג	Loss function	Loss function
מקור מכונן	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
כינויים	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
קשורות	3	3
תקציר	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.	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.