方法对比

	Log-Loss（交叉熵损失）	布里尔分数	F1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	1950	1979
提出者	Information theory and machine learning literature	Glenn W. Brier	C. J. van Rijsbergen
类型	Loss function	Loss function	Evaluation metric
开创性文献	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
别名	Cross-Entropy Loss, Logloss	Mean Squared Probability Error	F-measure, Harmonic Mean
相关	3	3	5
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.	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.