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基于可解释机器学习的制造业数字化转型的预测

Chen Zhu^1,2,3, Xue Liu¹, Dong Chen^4,5

¹Business School, Yangzhou University, Yangzhou, China.

|March 29, 2024

概括

这项研究使用机器学习来预测数字化转型的成功. 极端树分类算法准确地识别了关键因素,为业务发展提供了有针对性的策略.

科学领域:

工商管理工商管理
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

数字化转型对于现代企业发展至关重要.
预测建模可以优化数字化转型计划.
了解影响因素是成功实施的关键.

研究的目的:

开发用于数字转型的预测机器学习模型.
识别和分析影响数字转型的关键因素.
提出可行的战略,以加强企业的数字化转型.

主要方法:

四个机器学习算法的比较,选择极端树分类 (ETC) 以其预测准确度.
相关性分析和递归特征消除以确定关键影响因素.
沙普利添加式解释 (SHAP) 用于预测模型的可解释性分析.

主要成果:

极端树分类 (ETC) 算法表现出卓越的预测性能.
确定了影响数字化转型的关键特征的一个子集.
SHAP值提供了对每个关键特征对转型结果的影响的见解.

结论:

开发的预测模型和确定的关键特征为数字化转型提供了数据驱动的方法.
拟议的定量调整战略为企业提供了实际指导.
这项研究支持成功的数字化发展和业务增长的知情决策.

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