A machine learning-based analytical framework for employee turnover prediction

Abstract

Employee turnover (ET) can cause severe consequences to a company, which are hard to be replaced or rebuilt. It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET, allowing the human resource management team to take pro-active action for retention or plan for succession. However, building such a system faces challenges due to the variety of influential human factors, the lack of training data, and the large pool of candidate models to choose from. Solutions offered by existing studies only adopt essential learning strategies. To fill this methodological gap, we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering, model training and validation, and ensemble learning towards building an accurate and robust predictive model. The proposed framework is evaluated on two representative datasets with different sizes and feature settings. Results demonstrate the superior performance of the final model produced by our framework.