基于特征选择和机器学习的员工离职预测方法

Employee Turnover Prediction Method Based on Feature Selection and Machine Learning

在大数据时代背景下,企业如何利用大数据技术及时掌握员工职业动态,提前洞悉并预测员工的离职倾向,帮助人力资源团队更好地作出人才挽留或储备的应对策略,这对企业的发展至关重要。本文以Datacastle平台的员工离职数据集为研究对象,首先对数据进行预处理、相关性分析、特征构造、非数值数据和数值型数据的处理以及不平衡数据的处理等步骤,然后利用递归特征消除算法反复构建模型剔除特征,最后利用决策树模型、支持向量机模型、逻辑回归模型、XGBoost模型分别对员工离职倾向进行预测,并对各模型的预测结果进行对比分析。结果显示,将smote采样处理后的数据应用于XGBoost模型后,无论在预测的准确率、召回率还是AUC的表现上均优于其他三个模型,作为员工离职预测的分类模型效果最佳。以该模型计算各变量的重要性排序,并结合交叉统计图分析后得出,员工婚姻状况、所学习的专业领域、所在部门、股票期权水平等因素对员工是否离职的影响较高。In the era of digital economy, if enterprises make full use of science and technology to predict the turnover dynamics of employees and understand the turnover tendency of employees in advance, it can help the human resources team to make better coping strategies of talent retention or reserve, which has far-reaching significance for the development of enterprises. The results show that applying SMOTE-sampled data to the XGBoost model outperforms the other three models in terms of prediction accuracy, recall rate, and AUC. It proves to be the most effective classification model for employee turnover prediction. Based on the importance ranking of variables calculated by this model and analyzed with cross-statistical charts, it was found that factors such as the employee’s marital status, field of study, department, and stock option level have a significant impact on whether the employee leaves the company.