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Analysis of Influence Factors and Prediction for Employee Turnover

WANG Guanpeng, QIN Shuangyan, CUI Hengjian   

  1. School of Mathematical Sciences, Capital Normal University, Beijing 100048
  • Received:2021-06-11 Revised:2022-01-28 Online:2022-06-25 Published:2022-07-29

WANG Guanpeng, QIN Shuangyan, CUI Hengjian. Analysis of Influence Factors and Prediction for Employee Turnover[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1616-1632.

This article adopts high-dimensional variable screening method to make analysis of influence factors for employee turnover, as well as to predict the possibility of employee turnover. For high-dimensional data, MV (mean of variance, see Cui, et al. (2015)) method and LASSO method are used to select variables related to employee turnover, which can be entered the classification model. To ensure the prediction accuracy of the classification model, this paper uses four models including support vector machine, random forest, XGBoost and Logistic model to predict the possibility of employee turnover. In 100 experiments, compared to other 7 models combined with MV method, the average classification accuracy of the random forest model combined with the MV variable selection is more higher, as high as 95.43%. The above experimental results are verified by changing the ratio of training set to validation set, sampling 80% sample data, and adding random disturbances. It is found that the average classification accuracy of random forest model with MV method is still higher, this means the model has robustness.

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