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在人力资源分析中整合机器学习和可解释的人工智能来预测员工消耗

Maytha Al-Ali1, Majed Alwateer2, Shatha Abed Alsaedi2

  • 1College of Business, Zayed University, Dubai, 19282, UAE.

Scientific reports
|February 12, 2026
PubMed
概括

预测员工磨损对于员工留守至关重要. 本研究介绍了一种机器学习框架,使用SHAP (SHapley增量扩展) 来识别加班和工作满意度等关键驱动因素,从而实现主动的人才管理.

关键词:
数据平衡技术数据平衡技术预测员工磨损的预测.功能选择 功能选择超参数优化超参数优化工作变更分析工作变更分析.在人力资源分析中使用机器学习.可以解释SHAP的可解释性

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