Application of Decision Tree Algorithm in Housing Purchase Problems—A Case Study of Xining City  

作　　者：Siyu Chen Li Fu Siyu Chen;Li Fu(School of Mathematics and Statistics, Qinghai Nationalities University, Xining, China)

机构地区：[1]School of Mathematics and Statistics, Qinghai Nationalities University, Xining, China

出　　处：《Journal of Computer and Communications》2024年第11期173-186,共14页电脑和通信（英文）

摘　　要：Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, replacing the Gini coefficient with the correlation coefficient to consider the correlation between conditional attributes, prioritizing the selection of conditional attributes with higher correlation coefficients as leaf nodes. The collected data on homebuyers is divided into age groups, including youth, middle-aged, and elderly groups. Both traditional CART decision tree and improved CART decision tree are applied to this problem, and after comparison, it is found that the depth of the CART decision tree in this study is reduced, the number of leaf nodes is decreased, the time complexity is shortened, efficiency is improved, and pruning issues are avoided. Finally, corresponding housing recommendations are given to homebuyers of different ages.Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, replacing the Gini coefficient with the correlation coefficient to consider the correlation between conditional attributes, prioritizing the selection of conditional attributes with higher correlation coefficients as leaf nodes. The collected data on homebuyers is divided into age groups, including youth, middle-aged, and elderly groups. Both traditional CART decision tree and improved CART decision tree are applied to this problem, and after comparison, it is found that the depth of the CART decision tree in this study is reduced, the number of leaf nodes is decreased, the time complexity is shortened, efficiency is improved, and pruning issues are avoided. Finally, corresponding housing recommendations are given to homebuyers of different ages.

关 键 词：Decision Tree Gini Coefficient Correlation Coefficient 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]