摘要
以多元线性回归统计模型为基础,用Python语言对美国部分地区房价数据进行建模预测,进而探究提高多元回归线性模型精度的方法。先对数据进行探索性预处理,随后设置虚拟变量并建模得出预测结果,再使用方差膨胀因子对多重共线性进行修正,从而提高模型精度与稳健性,使回归结果在很大程度上得到优化。
Based on the multiple linear regression statistical model,this paper built the model to predict the house price by utilizing some parts of the United States data,and then explores methods to improve the accuracy of the multiple linear regression model.Firstly,the data were preprocessed,and then the dummy variables were set up for modeling to obtain the predicted results.After that,the multicollinearity was modified by variance inflation factor so that the accuracy as well as robustness of the model was improved,and the regression results were optimized largely.
作者
罗博炜
洪智勇
王劲屹
Luo Bowei;Hong Zhiyong;Wang Jingyi(Facalty of Intelligent Manufacturing,Wuyi University,Jiangmen,Guangdong 529020,China)
出处
《计算机时代》
2020年第6期51-54,共4页
Computer Era
基金
广东省自然科学基金项目(2016A030310003)
广东省高校高等教育教学改革项目“基于双螺旋结构模型的创新创业协同育人机制探索与实践”(GDJX2017011)
五邑大学2019年度省级大学生创新训练项目(S01911349055)。
关键词
多元线性回归
多重共线性
虚拟变量
方差膨胀因子
multiple linear regression
multicollinearity
dummy variable
variance inflation factor
作者简介
罗博炜(1998-),男,江西人,五邑大学智能制造学部学生,主要研究方向:数据挖掘、智能信息处理。
