방법 비교

	리프트 및 게인 차트	재현율 (Recall, 민감도)
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	1990s	20th century
창시자	Data mining and marketing analytics	Historical statistical foundations
유형	Evaluation visualization	Evaluation metric
원전	Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
별칭	Cumulative Gain Chart, Lift Curve	Sensitivity, True Positive Rate, TPR
관련	2	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.	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.