Binary confusion matrix.

The enhancement of digital transformation is of paramount importance for business development. This study employs machine learning to establish a predictive model for digital transformation, investigates crucial factors that influence digital transformation, and proposes corresponding improvement strategies. Initially, four commonly used machine learning algorithms are compared, revealing that the Extreme tree classification (ETC) algorithm exhibits the most accurate prediction. Subsequently, through correlation analysis and recursive elimination, key features that impact digital transformation are selected resulting in the corresponding feature subset. Shapley Additive Explanation (SHAP) values are then employed to perform an interpretable analysis on the predictive model, elucidating the effects of each key feature on digital transformation and obtaining critical feature values. Lastly, informed by practical considerations, we propose a quantitative adjustment strategy to enhance the degree of digital transformation in enterprises, which provides guidance for digital development.

数字化转型的深化对于企业发展至关重要。本研究借助机器学习构建数字化转型预测模型，探究影响数字化转型的关键因素，并提出相应优化策略。首先，本研究对四种常用机器学习算法开展对比实验，结果显示极端树分类（Extreme tree classification, ETC）算法具备最高的预测精度。随后，通过相关性分析与递归特征消去法，筛选得到影响数字化转型的关键特征，构建对应的特征子集。继而采用夏普利可加解释（Shapley Additive Explanation, SHAP）值对预测模型进行可解释性分析，阐明各关键特征对数字化转型的作用机制，并获取关键特征取值。最后，结合实际业务需求，本研究提出可量化的调整策略以提升企业数字化转型水平，为企业数字化发展提供有效指导。