방법 비교

	리프트 및 게인 차트	정밀도-재현율 AUC	재현율 (Recall, 민감도)
분야	모델 평가	모델 평가	모델 평가
계열	MCDM	MCDM	MCDM
기원 연도	1990s	2006	20th century
창시자	Data mining and marketing analytics	Davis and Goadrich	Historical statistical foundations
유형	Evaluation visualization	Evaluation metric	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
별칭	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve	Sensitivity, True Positive Rate, TPR
관련	2	4	5
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.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.