摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。据中国人民共和国北京的新闻报道，NewsRx记者称，“驾驶考试是检验学员是否具备国家课程规定的能力的一种方式。”我们的新闻记者从北京工业大学的研究中得到一句话：“因此，”探索影响驾驶考试成绩的关键因素对于帮助学习者掌握扎实的驾驶技能具有特别重要的意义。采用可解释性机器学习(ML),利用包括个人特征、训练模式、驾驶错误频率、扣分、驾驶技能、本文以我国某驾驶学校为研究对象，采用光梯度提升机(LightGBM)ML方法，建立了受试者2考试成绩预测模型，并采用SHapley Additi Explation(SHAP)，探讨了主要影响因素与驾驶行为的关系。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Beijing, People's Republ ic of China, by NewsRx journalists, research stated, "The driving test is the on ly way to verify that learner drivers have acquired the competencies stipulated in the national curriculum." Our news correspondents obtained a quote from the research from Beijing Universi ty of Technology: "Therefore, exploring the key factors that influence the outco me of the driving test is of particular importance in assisting learner drivers to gain solid behind-the-wheel skills. Interpretable machine learning (ML) is em ployed to analyze the probability of learner drivers' passing the driving skills test (called the Subject 2 test in China) using a data set comprising personal characteristics, training mode, frequency of driving errors, deducted points, pe rcentage of qualified training times, and score of constructed graphs related to driving behaviors. The data are collected from a driving school in China. A pre diction model of the Subject 2 test outcome is constructed by adapting the Light Gradient Boosting Machine (LightGBM) ML method. Furthermore, the SHapley Additi ve exPlanation (SHAP) is employed to explore the relationships between key influ encing factors and the aforementioned outcome."
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