Author:
Pholo Moanda Diana,Hamam Yskandar,Khalaf AbdelBaset,Tu Chunling
Publisher
Springer Nature Singapore
Reference27 articles.
1. Al Daoud, E.: Comparison between xgboost, lightgbm and catboost using a home credit dataset. Int. J. Comput. Inf. Eng. 13(1), 6–10 (2019)
2. Antel, K., Louw, V.J., Maartens, G., Oosthuizen, J., Chetty, D., Verburgh, E.: Diagnosing lymphoma in the shadow of an epidemic: lessons learned from the diagnostic challenges posed by the dual tuberculosis and hiv epidemics. Leukemia Lymphoma 61(14), 3417–3421 (2020)
3. Asselman, A., Khaldi, M., Aammou, S.: Enhancing the prediction of student performance based on the machine learning xgboost algorithm. Interact. Learn. Environ. 1–20 (2021)
4. Bentéjac, C., Csörgő, A., Martínez-Muñoz, G.: A comparative analysis of gradient boosting algorithms. Artif. Intel. Rev. 54(3), 1937–1967 (2021)
5. Cantini, L., Mentrasti, G., Russo, G., Signorelli, D., Pasello, G., Rijavec, E., Russano, M., Antonuzzo, L., Rocco, D., Giusti, R., et al.: Evaluation of covid-19 impact on delaying diagnostic-therapeutic pathways of lung cancer patients in Italy (covid-delay study): fewer cases and higher stages from a real-world scenario. ESMO Open 7(2), 100,406 (2022)