摘要
由新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细的机器学习数据已经呈现。根据NewsRx编辑对威海市的新闻报道，研究表明：“基于上层理论，我们利用一个大规模的中国上市公司数据，利用机器学习的方法来探讨CEO特征对公司业绩的影响。比较了10种机器学习方法。”"我们发现,极端梯度模型(XGBoost)在预测公司违规行为方面优于其他模型."这项研究的资金来自中国教育部。新闻记者引用山东大学的一篇研究，“一个结合XGBoost和SHapley加性解释（Shap）的解释模型表明，CEO特征在预测公司违规行为中起着核心作用，任期对公司违规行为的预测能力最强，与公司违规行为负相关，其次是营销经验、教育程度、学历（即同时担任董事长）。与此相反，持股、年龄、薪酬与公司违规行为呈正相关，我们还分析了违规严重程度和违规类型，证实了任期在预测更严重和更严重的违规行为中的作用。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Weihai, People's Republic of China, by NewsRx editors, research stated, "Based on upper echelon theory, we e mploy machine learning to explore how CEO characteristics influence corporate vi olations using a large-scale dataset of listed firms in China for the period 201 0-2020. Comparing ten machine learning methods, we find that eXtreme Gradient Bo osting (XGBoost) outperforms the other models in predicting corporate violations ." Financial support for this research came from Ministry of Education, China. Our news journalists obtained a quote from the research from Shandong University , "An interpretable model combining XGBoost and SHapley Additive exPlanations (S HAP) indicates that CEO characteristics play a central role in predicting corpor ate violations. Tenure has the strongest predictive power and is negatively asso ciated with corporate violations, followed by marketing experience, education, d uality (i.e., simultaneously holding the position of chairperson), and research and development experience. In contrast, shareholdings, age, and pay are positiv ely related to corporate violations. We also analyze violation severity and viol ation type, confirming the role of tenure in predicting more severe and intentio nal violations."
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