השוואת שיטות

	תרשים Lift and Gain	עֶרְכָּה (רגישות)
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1990s	20th century
הוגה השיטה	Data mining and marketing analytics	Historical statistical foundations
סוג	Evaluation visualization	Evaluation metric
מקור מכונן	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.
כינויים	Cumulative Gain Chart, Lift Curve	Sensitivity, True Positive Rate, TPR
קשורות	2	5
תקציר	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.