Version 1
: Received: 1 October 2024 / Approved: 1 October 2024 / Online: 2 October 2024 (13:42:29 CEST)
How to cite:
Yin, Z.; Hu, B.; Chen, S. Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models. Preprints2024, 2024100072. https://doi.org/10.20944/preprints202410.0072.v1
Yin, Z.; Hu, B.; Chen, S. Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models. Preprints 2024, 2024100072. https://doi.org/10.20944/preprints202410.0072.v1
Yin, Z.; Hu, B.; Chen, S. Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models. Preprints2024, 2024100072. https://doi.org/10.20944/preprints202410.0072.v1
APA Style
Yin, Z., Hu, B., & Chen, S. (2024). Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models. Preprints. https://doi.org/10.20944/preprints202410.0072.v1
Chicago/Turabian Style
Yin, Z., Baojun Hu and Shuhan Chen. 2024 "Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models" Preprints. https://doi.org/10.20944/preprints202410.0072.v1
Abstract
Abstract. Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R², making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
