قارن الطرق

	مخطط الرفع والكسب	مساحة تحت منحنى الدقة-الاستدعاء (PR AUC)	الاستدعاء (الحساسية)
المجال	تقييم النماذج	تقييم النماذج	تقييم النماذج
العائلة	MCDM	MCDM	MCDM
سنة النشأة	1990s	2006	20th century
صاحب الطريقة	Data mining and marketing analytics	Davis and Goadrich	Historical statistical foundations
النوع	Evaluation visualization	Evaluation metric	Evaluation metric
المصدر التأسيسي	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.
الأسماء البديلة	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve	Sensitivity, True Positive Rate, TPR
ذات صلة	2	4	5
الملخص	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.