大数据挖掘的分类算法应用——以XGBoost为例

Application of the classification algorithm for big data mining:taking XGBoost as an example

文章基于常德职业技术学院信息高度共享的数据中心,探索了大数据挖掘分类算法XGBoost在学生管理中的应用。通过数据中心,抽取学生在各类业务系统中的多个维度特征作为算法数据来源,为算法模型的高准确率和高可解释性提供了坚实的基础。XGBoost具有训练速度快、拟合能力强且能较好地避免过拟合等特点,已经成为当前数据挖掘和机器学习领域中最常用的算法之一。通过实验结果分析,利用大数据挖掘分类算法合理使用学校的大数据,可以帮助学校更好地管理学生。

Based on the highly shared data center of Changde Vocational and Technical College,this paper explores the application of big data mining classification algorithm XGBoost in school student management.Through the data center,multiple dimensional characteristics of students in various business systems are extracted as the source of the algorithm data,which provides a solid foundation for the high accuracy and high interpretability of the algorithm model.XGBoost has the characteristics of fast training speed,strong fitting ability and better avoiding overfitting,and has become one of the most commonly used algorithms in the current data mining and machine learning fields.Through the analysis of experimental results,using big data mining classification algorithm to use school big data can help schools to better manage students.