방법 비교

	리프트 및 게인 차트	정밀도-재현율 AUC
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1990s	2006
창시자	Data mining and marketing analytics	Davis and Goadrich
유형	Evaluation visualization	Evaluation metric
원전	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.
별칭	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve
관련	2	4
요약	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.