Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting

Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover. In this paper, the problem of employee turnover and the key machine learning algorithms that have been used to solve it are discussed. The novel contribution of this paper is to explore the application of extreme gradient boosting (XGBoost) as an improvement on these traditional algorithms, specifically in its ability to generalize on noise-ridden data which is prevalent in this domain. This is done by using data from the HRIS of a global retailer and treating the attrition problem as a classification task and modeling it using supervised techniques. The conclusion is reached by contrasting the superior accuracy of the XGBoost classifier against other techniques and explaining the reason for its superior performance. This paper is structured as follows. Section II gives a brief overview of the employee turnover problem, the importance of solving it, and the historical work done in terms of application of machine learning techniques to solve this problem. Section III explores the 7 different supervised techniques, including XGBoost, that this paper compares. Section IV outlines the experimental design in terms of the characteristics of the dataset, pre-processing, cross-validation, and the choice of metrics for accuracy comparison. Section V showcases the results of the study and its subsequent discussion. Section VI concludes the paper by recommending the XGBoost classifier for predicting turnover.