基于机器学习的企业员工流失预警分析  

Early Warning Analysis of Enterprise Employee Turnover Based on Machine Learning

作　　者：柴天潇 钱雨欣 王文 

机构地区：[1]天津商业大学,天津

出　　处：《现代管理》2022年第12期1617-1629,共13页Modern Management

摘　　要：企业培养人才需要大量的成本,为了降低企业成本,降低人才流失率,应当注重企业员工流失分析。员工流失分析是评估公司员工流动率的过程,本文基于Kaggle平台分享的数据集,在数据探索性分析和预处理的基础上,采用多种机器学习算法,构建企业员工流失预警模型,并进行模型的比较评价,目的是找到影响员工流失的主要因素,预测未来的员工离职状况,减少重要价值员工流失。Enterprises need a lot of costs to cultivate talents. In order to reduce the cost of enterprises and the rate of brain drain, we should pay attention to the analysis of employee turnover. Employee turnover analysis is the process of evaluating the company’s employee turnover rate. Based on the data set shared on the Kaggle platform, this paper uses a variety of machine learning algorithms to build an early warning model for enterprise employee turnover, compare and evaluate the models. The purpose is to find the main factors that affect employee turnover, predict future employee turnover, and reduce the loss of employees with important values.

关 键 词：机器学习 企业员工流失预警 决策树模型 GBDT模型 XGBoost模型 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]