6卷 第6期
1.School of Humanities and Education, Guangzhou Institute of Science and Technology,Guangzhou, 510540, China
2.School of Computer Science and Engineering, Guangzhou Institute of Science and Technology,Guangzhou, 510540, China
纸质出版日期：2025-06-30
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(2025). Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost. 科技理论与实践（英文版）, 6(6), 92-101.
Xiao Xiao,Zhong XueYang.Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost[J].Theory and Practice of Science and Technology,2025,06(06):92-101.
(2025). Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost. 科技理论与实践（英文版）, 6(6), 92-101. DOI： 10.47297/taposatWSP2633-456919.20250606.
Xiao Xiao,Zhong XueYang.Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost[J].Theory and Practice of Science and Technology,2025,06(06):92-101. DOI： 10.47297/taposatWSP2633-456919.20250606.
In the highly competitive global automotive industry
the stability of front-line workers in auto parts manufacturing enterprises is vital for the industry's supply chain
but their high turnover rate has become a bottleneck restricting enterprises' sustainable development. This study constructs a front-line worker turnover risk prediction model for a South China auto parts manufacturer based on the CatBoost algorithm. By collecting 28 features from multiple management systems and expert evaluations
performing data preprocessing
hyperparameter tuning
and model comparison
the CatBoost model shows significant advantages in handling categorical features and preventing overfitting. Its prediction accuracy reaches an AUC of 0.92 and an F1 score of 0.886 after oversampling
far surpassing traditional models such as logistic regression and random forest. Feature importance analysis reveals that factors such as job position and seniority significantly impact turnover risk. Targeted interventions based on model predictions have reduced the quarterly turnover rate from 6.3% to 4.5%
and in peak periods from 8.3% to 6.1%. This research provides an innovative and practical solution to address the turnover of front-line workers
with future studies to further optimize the model and verify its long-term effectiveness.
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