[
Breiman, L., Friedman, J. H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees, Chapman & Hall/CRC.
]Search in Google Scholar
[
Burkov, A. 2019. The Hundred Page Machine Learning Book, Andriy Burkov.
]Search in Google Scholar
[
Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM
]Search in Google Scholar
[
Giussani, A., 2019. Applied Machine Learning with Python, Egea S.p.A. SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
]Search in Google Scholar
[
Guo, W., Zhou, Z.Z., 2022. A comparative study of combining tree-based feature selection methods and classifiers in personal loan default prediction. Journal of Forecasting, 41, 1248-1313.
]Search in Google Scholar
[
James, G., Witten, D., Hastie, T., Tibshirani, R., 2021. An Introduction to Statistical Learning with Applications in R, 2nd ed., Springer, New York.
]Search in Google Scholar
[
Jhaveri, S., Khedkar, I., Kanharia, Y., Jaswal, S. 2019. Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns, Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019), 27-29 March, Erode, India, 1170-1173.
]Search in Google Scholar
[
Jiang, H., 2021. Machine Learning Fundamentals, Cambridge University Press.
]Search in Google Scholar
[
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y., 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 31st Conference on Neural Information Processing Systems (NIPS 2017).
]Search in Google Scholar
[
LI, Y., 2019. Credit Risk Prediction Based on Machine Learning Methods. 14th International Conference on Computer Science & Education (ICCSE 2019), Toronto, Canada, 19-21 August 2019, 1011-1013.
]Search in Google Scholar
[
Liang, Y., Wu, J., Wang, W., Cao, Y., Zhong, B., Chen, Z., Li,Z. 2019. Product Marketing Prediction based on XGboost and LightGBM Algorithm, Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition AIPR 2019, August 16–18, Beijing, China, 150-153.
]Search in Google Scholar
[
MELENDEZ, R., 2019. Credit Risk Analysis Applying Machine Learning Classification Models. Intelligent Computing - Proceedings of the Computing Conference CompCom 2019, Advances in Intelligent Systems and Computing, vol.997, 804-814.
]Search in Google Scholar
[
Pandey, T.N., Mohapatra, S.K., Jagadev, A.K., Dehuri, S., 2017. Credit Risk Analysis using Machine Learning Classifiers. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), Chennai, India, 1-2 August 2017, 1850-1854.
]Search in Google Scholar
[
Pillai, S.G., Woodbury, J., Dikshit, N., Leider, A., Tappert, C.C., 2019. Credit Risk Analysis Applying Machine Learning Classification Models. Proceedings of the Future Technologies Conference (FTC) 2019, Advances in Intelligent Systems and Computing, vol.1069, 107-126.
]Search in Google Scholar
[
Ponsam, J.G., Bella Gracia, S.V.J., Geetha, G., Karpaselvi, S., Nimala, K., 2021. Credit Risk Analysis using LightGBM and a comparative study of popular algorithms. 4th International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 16-17 December, 634-641.
]Search in Google Scholar
[
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A., 2018. Catboost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems 31 (NeurIPS 2018).
]Search in Google Scholar
[
Qiu, Z., Li, Y., Ni, P., Li, G., 2019. Credit Risk Scoring Analysis Based on Machine Learning Models. 6th International Conference on Information Science and Control Engineering (ICISCE) Technologies, Shangai, China, 20-22 December 2019, 220-224.
]Search in Google Scholar
[
Tian, Z., Xiao, J., Feng, H., Wei, Y., 2020. Credit Risk Assessment based on Gradient Boosting Decision Tree. Procedia Computer Science, 174, 150-160.
]Search in Google Scholar
[
Turjo, A.A., Rahman, Y., Karim, S.M., Biswas, T.H., Dewan, I., Hossain, M.I., 2021. CRAM: A Credit Risk Assessment Model by Analyzing Different Machine Learning Algorithms. 4th International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 30-31 August, 125-130.
]Search in Google Scholar
[
Wang, Y., Zhang, Y., Lu, Y., Yu, X., 2020. A Comparative Assessment of Credit Risk Model Based on Machine Learning – a case study of bank loan data. Procedia Computer Science, 174, 141–149.
]Search in Google Scholar
[
Wang, Y., Lu, J., Qin, J., Zhang, C., Chen, Y., 2020. The Application Study of Credit Risk Model In Financial Institution via Machine-learning Algorithms. 7th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 18-20 December 2020, 1419-1428.
]Search in Google Scholar
[
Xie. Y., Xiang, W., Ji, M., Peng, J., Huang, Y. 2019. Application analysis of predicting monthly house rental based on XGBoost and LightGBM algorithms, Comput. Appl. Softw., 36(9), 151–155.
]Search in Google Scholar
[
Yalcin, M., Bagdatli Kalkan, S. 2022. Determining the best estimation model with tree-based machine learning methods: implementation on customer spendings for e-commerce websites, Advances and Applications in Statistics, 75, 91-109.
]Search in Google Scholar
[
Zhang, D., Gong, Y. 2020. The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure, IEEE Access, 8, 220990-221003.
]Search in Google Scholar
[
Zhang, Y., Zhao, Z., Zheng, J. 2020. CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China, Journal of Hydrology, 588, 125087.
]Search in Google Scholar