用机器学习评估棒球教学效果，精准预测学生成绩并优化训练策略。
Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning
- 构建多特征机器学习模型，预测棒球训练综合评分。
- K近邻与梯度提升回归器表现最佳，误差更低且更稳定。
- 发现击球次数和跑垒次数是影响成绩的关键因素，适合体育教学优化。
在现代体育教育中，数据驱动的评价方法日益受到关注，尤其体现在通过机器学习模型对学生成绩进行量化预测。本研究旨在利用多种机器学习模型回归并预测学生在棒球训练中的综合评分，以评估当前棒球教学方法的有效性，并提出针对性的训练优化建议。通过收集击球次数、跑垒时间、击球表现等多项特征建立模型。实验结果表明，K-近邻回归器（K-Neighbors Regressor）与梯度提升回归器（Gradient Boosting Regressor）在综合预测准确性和稳定性方面表现优异，其R²得分与误差指标显著优于其他模型。进一步特征重要性分析显示，累计击球数和累计跑垒数是影响学生综合评分的关键因素。基于此，本文提出了优化训练策略的建议，表明数据驱动的教学评估方法可有效支持体育教育，推动个性化与精细化教学方案设计。
原文摘要 · Abstract (English)
In modern physical education, data-driven evaluation methods have gradually attracted attention, especially the quantitative prediction of students' sports performance through machine learning model. The purpose of this study is to use a variety of machine learning models to regress and predict students' comprehensive scores in baseball training, so as to evaluate the effectiveness of the current baseball teaching methods and put forward targeted training optimization suggestions. We set up a model and evaluate the performance of students by collecting many characteristics, such as hitting times, running times and batting. The experimental results show that K-Neighbors Regressor and Gradient Boosting Regressor are excellent in comprehensive prediction accuracy and stability, and the R score and error index are significantly better than other models. In addition, through the analysis of feature importance, it is found that cumulative hits and cumulative runs are the key factors affecting students' comprehensive scores. Based on the results of this study, this paper puts forward some suggestions on optimizing training strategies to help students get better performance in baseball training. The results show that the data-driven teaching evaluation method can effectively support physical education and promote personalized and refined teaching plan design.
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