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.

员工流失 (ET) 会对公司造成严重后果，难以替代或重建。因此，开发一个可以准确预测 ET 可能性的智能系统至关重要，使人力资源管理团队能够采取积极的行动来保留或计划继任。然而，由于影响人为因素的多样性、训练数据的缺乏以及可供选择的候选模型池庞大，构建这样一个系统面临挑战。现有研究提供的解决方案仅采用基本的学习策略。为了填补这一方法上的空白，我们提出了一种基于机器学习的分析框架，该框架采用简化的方法进行特征工程、模型训练和验证以及集成学习，以构建准确且稳健的预测模型。建议的框架在两个具有不同大小和特征设置的代表性数据集上进行评估。结果证明了我们的框架生成的最终模型的卓越性能。