方法对比

	Log-Loss（交叉熵损失）	准确率	布里尔分数
领域	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM
起源年份	1990s	20th century	1950
提出者	Information theory and machine learning literature	Historical statistical foundations	Glenn W. Brier
类型	Loss function	Evaluation metric	Loss function
开创性文献	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.
别名	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate	Mean Squared Probability Error
相关	3	5	3
摘要	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.	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.	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.