方法对比

	布里尔分数	准确率	Log-Loss（交叉熵损失）	平均绝对误差 (MAE)
领域	模型评估	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM	MCDM
起源年份	1950	20th century	1990s	1799
提出者	Glenn W. Brier	Historical statistical foundations	Information theory and machine learning literature	Pierre-Simon Laplace
类型	Loss function	Evaluation metric	Loss function	Robust distance-based metric
开创性文献	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.
别名	Mean Squared Probability Error	Overall Accuracy, Correct Classification Rate	Cross-Entropy Loss, Logloss	MAE, L1 error, mean absolute deviation
相关	3	5	3	3
摘要	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.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.