决策树和渐变式学习集群用于预测员工流动率
1School of Labor Economics, Capital University of Economics and Business, Beijing, China.
PeerJ. Computer science
|December 9, 2024
概括
使用基于梯度的决策树来预测员工周转率可以降低业务成本. 这种计算方法为业务分析中的战略决策提供了可扩展,准确的见解.
科学领域:
- 业务分析 业务分析
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 员工周转率显著影响企业利能力.
- 预测建模为减轻营业额成本提供了一个潜在的解决方案.
- 人力资源分析数据对于了解员工退出模式至关重要.
研究的目的:
- 调查计算进步的可行性,以预测员工周转率.
- 开发和评估用于预测员工消耗的预测模型.
- 评估这些模型在业务决策支持系统中的有用性.
主要方法:
- 使用HR Analytics数据集进行分析.
- 开发了一组基于梯度的决策树.
- 将性能与其他复杂的预测技术进行比较.
主要成果:
- 基于梯度的决策树组合准确地预测了员工周转率.
- 提出的方法优于现有的复杂技术.
- 在处理结构化和不平衡数据方面表现出卓越的表现.
结论:
- 基于梯度的决策树为员工流动率预测提供了可扩展和高效的解决方案.
- 这种方法有效地平衡了业务分析的预测准确性和计算效率.
- 调查结果为明智的战略商业决策提供了可靠的见解.
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