Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost

Xiao Xiao; XueYang Zhong

doi:10.47297/taposatWSP2633-456919.20250606

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Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost

Volume 6, Issue 6

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Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost
Research on the Prediction of First-Line Workers' Turnover Risk in Auto Parts Manufacturing Companies Based on Catboost
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
Vol. 6, Issue 6, Pages: 92-101(2025)
Published：30 June 2025
DOI：10.47297/taposatWSP2633-456919.20250606
Accepted：

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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：

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.

摘要

Abstract

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.

关键词

Keywords

references

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