| dc.description.abstract | With the rise of big data, the human resource management department of some benchmark enterprises have started to use Auto ML platform and introduced machine learning to build models by applying algorithms for employee turnover rate management. This study analyzed the employee data of Jiangsu plant in China of Taiwanese Company C. Using three supervised machine learning classification algorithms, namely, gradient boosting, extreme gradient boosting (XGBoost) and random forest to build a voluntary turnover prediction model, and processed it by cross-validation.
The results showed that the AUC of both gradient boosting and extreme gradient boosting (XGBoost) models were above 0.8, indicating that the models had good discriminative ability and could effectively predict whether employees have a tendency to leave.
The key influencing factors of multi-variables that affect employee turnover and the characteristics of the high-risk group were also investigated. Between 18 independent variables, 7 key factors were found: being in level 6 position, getting the average performance score of 2.83 or less, having 11.4 or less years of experience, being in non-supervisory position, holding a posititon title of engineer or administrator, and household being registered in Jiangsu Province. Among them, the most likely to leave is the employee who is in level 6 position, and the second highest is the employee with an average performance score of 2.83 or below.
In addition, the results showed that the characteristics of the high-risk group are the ones who are in level 6 position, live in the local area and are graduated from university. The results of this study are summarized to provide Company C a reference for employee turnover rate management. | en_US |