李强, 翟亮.基于Stacking算法的员工离职预测分析与研究[J].重庆工商大学学报(自然科学版),2019,36(1):117-123
基于Stacking算法的员工离职预测分析与研究
Analysis and Research on Employee Turnover Prediction Based on Stacking Algorithm
  
DOI:
中文关键词:  Adaboost  Random Forest  Stacking  员工离职预测  主要因素
英文关键词:Adaboost  Random Forest  Stacking  employee turnover forecast  main factor
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作者单位
李强, 翟亮 兰州财经大学 电子商务综合重点实验室兰州 730020 
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中文摘要:
      针对员工离职会增加企业运营成本,降低企业盈利能力的问题,提出使用机器学习的离职员工预测算法;通过Stacking集成学习算法组合Adaboost和Random Forest基本算法构建LRA预测模型,实现对某企业的员工离职预测;实验结果显示,LRA模型的预测准确率为89.09%,相对于单一算法所构建验证的模型预测准确率明显提高, LRA模型的查准率、查全率以及F1度量指标证实模型的可行性与可靠性,通过对输入LRA模型的特征进行重要性排序,得到影响员工离职的主要因素有加班、工龄(0-3年)、收入、职业级别等,丰富已有研究的结论,有利于企业决策者,针对离职行为进行合理决策。
英文摘要:
      Aiming at the problem that employee turnover will increase the operating cost and reduce the profitability of the enterprise, this paper proposes the prediction algorithm of the resigned employees using machine learning algorithm. The LRA prediction model is constructed by combining Adaboost and Random Forest basic algorithms with Stacking ensemble algorithm to predict employee turnover in an enterprise. Experimental results show that the prediction accuracy of LRA model is 89.09%, which is significantly higher than that of the model constructed by a single algorithm. Precision, recall and F1 metrics confirm the feasibility and reliability of the model.By sorting the importance of the input LRA model’s features, the main factors affecting employee resignation are overtime, length of service (0-3 years), income, occupational level, etc., this method enriches the conclusions of existing studies and is beneficial to enterprise decision-makers to make reasonable decisions on employee turnover behavior.
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