Salīdzināt metodes

	Diagrammas pacēlums un ieguvums	Precīzijas un atsaukuma AUC
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	1990s	2006
Autors	Data mining and marketing analytics	Davis and Goadrich
Tips	Evaluation visualization	Evaluation metric
Pirmavots	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.
Citi nosaukumi	Cumulative Gain Chart, Lift Curve	PR AUC, PR Curve
Saistītās	2	4
Kopsavilkums	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.