Porównaj metody

	Wykresy lift i gain	Precyzja-Recall AUC	Czułość (Recall)
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM
Rok powstania	1990s	2006	20th century
Twórca	Data mining and marketing analytics	Davis and Goadrich	Historical statistical foundations
Typ	Evaluation visualization	Evaluation metric	Evaluation metric
Źródło pierwotne	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.
Inne nazwy	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve	Sensitivity, True Positive Rate, TPR
Pokrewne	2	4	5
Podsumowanie	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.