| （广州商学院 信息技术与工程学院，广东 广州 510363） 摘 要：基于大数据对大学生体质进行分类预测，有助于大学体育治理体系的建设，朴素贝叶斯模型是一种操作简单且性能较好的机器学习分类算法。基于朴素贝叶斯分类算法，采用广州商学院2014、2015年学生体测数据及其评分结果作为源数据，构建大学生体质分类器。应用此分类器可对大学生的体质状况实现一定概率意义上正确的判断，从而可以对体质存在隐患概率比较大的学生给出主动性预警，以便大学体育对学生进行群体性的体质判断、进行个性化的有效干预，从而促进学生健康发展，提高大学生整体体质水平。分类器模型用Python编码实现，最后用与训练数据不重叠的历史体质数据检测分类器的准确率，结果显示，基于朴素贝叶斯算法的体质分类器达到了78%的正确率。 |
DU Yun-mei，LIU Dong
| （Department of Information Technology and Engineering，Guangzhou Business College，Guangzhou 510363，China) Abstract: Based on big data, the authors carried out classification and prediction on university student fitness, which is conducive to university sports governance system construction; the Naïve Bayes model is a machine learn-ing classification algorithm that is simple to operate and provided with good performance. Based on Naive Bayes classification algorithm, and using the physical test data of classes 2014 and 2015 students of Guangzhou Business College and their score results as source data, the authors established a university student fitness classifier. By ap-plying such a classifier, researchers can, in a certain sense of probability, correctly determine newly or previously enrolled university students’ fitness condition, thus give a proactive early warning to those students whose fitness has a relatively high probability of hidden troubles, so that university physical education can carry out group fitness determination and individualized effective intervention on the students, thus promoting student healthy development and improving university students’ overall fitness level. The classifier mode was realized by using Python coding, in the end, the classifier’s accuracy rate was verified by using historical fitness data that did not overlap with training data, and the result showed that the fitness classifier based on naïve Bayes algorithm reached a correct rate of 78%. |
下载本期全文：点击下载