BUILDING A SCORING MODEL FOR FINANCIAL INSTITUTIONS USING THE XGBOOST MACHINE LEARNING ALGORITHM
DOI:
https://doi.org/10.30888/2709-2267.2024-25-00-004Ключові слова:
model validation, feature engineering, machine learning, predictive analytics, scoring model.Анотація
The construction of a credit scoring model using machine learning methods for determining the reliability of clients when making loan agreements by financial institutions has been considered. The application of the XGBoost algorithm is thoroughly investigMetrics
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Посилання
E Deng,H, Runger, G., Tuv, E. (2011). Bias of importance measures for multi-valued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN). pp. 293–300.
Hastie, T., Tibshirani, R., Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer Verlag.
Опубліковано
2024-07-30
Як цитувати
Волков, О., & Войналович, Н. (2024). BUILDING A SCORING MODEL FOR FINANCIAL INSTITUTIONS USING THE XGBOOST MACHINE LEARNING ALGORITHM. Sworld-Us Conference Proceedings, 1(usc25-00), 7–15. https://doi.org/10.30888/2709-2267.2024-25-00-004
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