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Research on Enterprise Digital-Level Classification Based on XGBoost Model
Digital knowledge and information have become significant production variables that have permeated all aspects of life and play a leading and supporting role in the growth of the real economy as the digital economy has developed. Through field research and web research, this study identifies digital-economy-related enterprises as the survey object; summarizes the fundamental information for these enterprises, their level of digitization, and the dilemma and demands of digital-level advancement; and generates survey data for 1936 enterprises. On the basis of these data, this study extracts the elements that influence the improvement of the enterprises’ digital level, applies statistical knowledge and machine learning techniques, and derives an enterprise digitization level index system and associated index score for enterprise digitization level. The experimental results indicate that the region, the time of establishment, the nature of ownership, the number of employees, R&D investment, being a national high-tech enterprise, and the establishment of digital transformation management departments have major effects. The AUC value of the XGBoost model modeled using all feature variables has achieved certain results, and the five assessment indices of the model have been enhanced to varying degrees, with the AUC being 0.9263.
Research on Enterprise Digital-Level Classification Based on XGBoost Model
Digital knowledge and information have become significant production variables that have permeated all aspects of life and play a leading and supporting role in the growth of the real economy as the digital economy has developed. Through field research and web research, this study identifies digital-economy-related enterprises as the survey object; summarizes the fundamental information for these enterprises, their level of digitization, and the dilemma and demands of digital-level advancement; and generates survey data for 1936 enterprises. On the basis of these data, this study extracts the elements that influence the improvement of the enterprises’ digital level, applies statistical knowledge and machine learning techniques, and derives an enterprise digitization level index system and associated index score for enterprise digitization level. The experimental results indicate that the region, the time of establishment, the nature of ownership, the number of employees, R&D investment, being a national high-tech enterprise, and the establishment of digital transformation management departments have major effects. The AUC value of the XGBoost model modeled using all feature variables has achieved certain results, and the five assessment indices of the model have been enhanced to varying degrees, with the AUC being 0.9263.
Research on Enterprise Digital-Level Classification Based on XGBoost Model
Qiuxia Ren (author) / Jigan Wang (author)
2023
Article (Journal)
Electronic Resource
Unknown
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