手法を比較

	ブライアースコア	Log-Loss（交差エントロピー損失）
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	1950	1990s
提唱者	Glenn W. Brier	Information theory and machine learning literature
種類	Loss function	Loss function
原典	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.
別名	Mean Squared Probability Error	Cross-Entropy Loss, Logloss
関連	3	3
概要	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.