用机器学习分析员工流失，提前预警并制定留人策略。
Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
- 整合多源人力资源数据，通过特征工程捕捉流失影响因素。
- 模型准确识别高流失风险员工，提升预测可靠性。
- 结合SHAP解释性分析，帮助企业管理者制定有效留人措施。
本文提出一种基于机器学习与数据工程的新型数据驱动方法，用于缓解员工流失问题。该框架整合来自多个人力资源系统的数据，采用先进的特征工程方法，全面捕捉影响员工离职的关键因素。研究构建了稳健的建模流程，解决了数据不平衡、类别型数据处理及模型可解释性等挑战。方法包括严谨的训练测试划分、基线模型建立以及概率校准的预测模型开发。特别强调使用SHAP值等可解释性技术，为组织提供可操作的洞察。算法选择、超参数调优和概率校准等关键设计决策均被深入讨论。该方法使企业能够主动识别离职风险，并制定针对性的保留策略，从而降低员工流失率。
原文摘要 · Abstract (English)
This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu
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