השוואת שיטות

	ציון ברייר	Log-Loss (Cross-Entropy Loss)	שגיאה מוחלטת ממוצעת (MAE)
תחום	הערכת מודלים	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM	MCDM
שנת המקור	1950	1990s	1799
הוגה השיטה	Glenn W. Brier	Information theory and machine learning literature	Pierre-Simon Laplace
סוג	Loss function	Loss function	Robust distance-based metric
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.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
כינויים	Mean Squared Probability Error	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
קשורות	3	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.	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.