পদ্ধতির তুলনা করুন

	লিফট এবং গেইন চার্ট	Recall (Sensitivity)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1990s	20th century
প্রবর্তক	Data mining and marketing analytics	Historical statistical foundations
ধরন	Evaluation visualization	Evaluation metric
মৌলিক উৎস	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.
অপর নাম	Cumulative Gain Chart, Lift Curve	Sensitivity, True Positive Rate, TPR
সম্পর্কিত	2	5
সারসংক্ষেপ	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.