方法对比

	提升和增益图	精确率-召回率曲线下面积
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1990s	2006
提出者	Data mining and marketing analytics	Davis and Goadrich
类型	Evaluation visualization	Evaluation metric
开创性文献	Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer.	Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.
别名	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve
相关	2	4
摘要	Lift and gain charts visualize classifier performance by showing how much better the model performs compared to random selection, particularly useful for ranking or scoring tasks where you select a top percentage of samples. They are widely used in marketing, credit scoring, and fraud detection.	The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.