방법 비교

	로그 손실(교차 엔트로피 손실)	Brier Score
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1990s	1950
창시자	Information theory and machine learning literature	Glenn W. Brier
유형	Loss function	Loss function
원전	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.
별칭	Cross-Entropy Loss, Logloss	Mean Squared Probability Error
관련	3	3
요약	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.