%0 Journal Article
%T 基于Logistic回归及机器学习方法对IBM员工流失因素的实证分析<br>Prediction and Comparative Analysis of IBM Employee Turnover Based on Logistic Regression and Machine Learning
%A 常沐冉
%A 黄坷杰
%A 张元巨
%A 王志
%J Advances in Applied Mathematics
%P 1420-1427
%@ 2324-8009
%D 2022
%I Hans Publishing
%R 10.12677/AAM.2022.113155
%X 通过建立Logistic回归模型和机器学习模型对IBM员工的基本特征进行分析。基于员工的年龄、部门、受教育程度等特征值，处理相关数据，设置虚拟变量，进一步构建Logistic回归模型及机器学习模型，实证分析各类特征值对员工流失率是否有显著影响，并找出最优拟合及预测效果。同时，从这些相关因素出发，针对不同属性的员工提出相应的解决方法，降低员工的流失率。最后，对不同方法的模型进行比较，为模型的可靠性提供参考标准。<br />
The basic characteristics of IBM employees are analyzed by Logistic regression model and machine learning model. Based on the employee’s age, department, education level and other characteristic values, the logistic regression model and machine learning model are constructed to analyze and predict whether the employee loses. At the same time, it analyzes the correlation between various factors and employee turnover rate, and puts forward corresponding solutions based on these factors, so as to ensure that employees have a sense of belonging, improve their working conditions and reduce the employee turnover rate. Finally, different models are compared to select the most appropriate model to provide a more accurate reference for predicting the turnover of employees.
%K Logistic回归模型，机器学习，员工流失<br>Logistic Regression Model
%K Machine Learning
%K Staff Turnover
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=49845