Article Details

Title · Authors · Abstract · Keywords · DOI · References

Risk Analysis of Employee Turnover Based on the Cox-RSF Model

GENG Yu

(School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China)

Section：Information Application · Vol. 2025, Issue 1 · Pages：161-165,170 · 2096-4706（2025）01-0161-06 · CLC number：TP391 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.01.031

Abstract

In order to improve the prediction accuracy of employee turnover, this paper uses the Cox proportional hazard model and the Random Survival Forest (RSF) model in survival analysis to construct and analyze a new combined model. Firstly, the original data is preprocessed, and the key variables are screened out by the Lasso method. Secondly, the weight coefficients of the selected variables are calculated by the proportional risk coefficient transformation. Finally, considering the advantages of the RSF model in the aspect of dealing with nonlinear data, the converted data is input into the RSF model for analysis. The research results show that the Cox-RSF model has higher prediction accuracy than the single Cox proportional hazard model and RSF model. In addition, the comparison with the traditional Logistic model further confirms the advantages of the new combined model in processing time-to-event type of data.

Keywords

employee turnover; Cox proportional hazard regression; RSF model

References

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Funding

Provincial Natural Science Research Project of Anhui Provincial Universities (2022AH050247); Research Project of Anhui Jianzhu University (2016QD118)

About the authors

Geng Yu (2000-), female, Han ethnicity, from Chuzhou, Anhui Province, is currently pursuing a master's degree in applied statistics.