在人力资源分析中,可解释的消耗风险评分用于管理层保留决策
M S Pavithran1, S M Vadivel2,3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Frontiers in big data
|January 28, 2026
概括
预测分析可以帮助人力资源经理减少员工流动. 这项研究开发了一个校准和可解释的模型来识别有风险的员工,从而实现积极的保留策略.
科学领域:
- 数据科学数据科学数据科学
- 人力资源管理 人力资源管理
- 组织行为 组织行为
背景情况:
- 员工流动对组织来说是一个重大的运营挑战.
- 预测分析为人力资源经理提供了一个潜在的解决方案,以预测和减轻营业额.
- 在这个领域,关于管理预测的可解释性和可靠性的研究有限.
研究的目的:
- 开发和评估一个可解释的员工消耗预测模型.
- 为人力资源经理提供数据驱动的洞察力,以积极主动地管理员工.
- 调查模型校准对预测准确性的影响.
主要方法:
- 使用了员工磨损数据集与预处理技术 (标签编码,特征缩放,SMOTE).
- 使用网格搜索和交叉验证训练和优化机器学习模型 (随机森林).
- 应用了Sigmoid校准,LIME,换特征的重要性,以及SHAP用于可解释性和风险评估.
主要成果:
- 随机森林分类器获得了高AUC-ROC得分97.37%.
- 模型校准显著降低了屏障评分,表明了更好的概率准确性.
- 视觉化识别了具有最高磨损风险的员工.
结论:
- 一个经过校准,可解释和风险分层的模型可以增强人力资源对保留策略的决策.
- 这种数据驱动的框架有助于从反应性到主动性劳动力管理的转变.
- 利用预测分析使人力资源领导者能够提高员工留守率和运营效率.
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