基于多元线性回归与随机森林算法的房价预测模型对比研究  

Comparative Research on House Price Forecast Model Based on Multiple Linear Regression and Random Forest Algorithm

作　　者：秦艳姣 QIN Yanjiao(School of Computer,Hubei University of Education,Wuhan 430205,China)

机构地区：[1]湖北第二师范学院计算机学院,湖北武汉430205

出　　处：《现代信息科技》2024年第22期127-131,共5页Modern Information Technology

摘　　要：为分析房价影响因素,选择更优的机器学习模型预测房价走势,使用两种机器学习算法构建了两种预测模型并对比预测效果。通过对公开数据集进行特征分析、预处理以及数据集划分,构建了多元线性回归预测模型和随机森林预测模型。采用平均绝对误差(MAE)、均方根误差(RMSE)以及决定系数(R2)作为最终评估模型的指标,结果显示随机森林模型的平均绝对误差、均方根误差较小,决定系数较大。评估得出随机森林模型预测准确性较多元线性回归模型更优。To analyze the influencing factors of house price and select a better Machine Learning model for forecasting house price trend,two forecasting models are constructed using two Machine Learning algorithms and the forecasting results are compared.The Multiple Linear Regression forecasting model and Random Forest forecasting model are constructed by analyzing features of public datasets,preprocessing,and partitioning datasets.Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and R-squared(R2)are used as indicators for evaluating thefinal model.The results indicate that the Random Forest model has lower MAE and RMSE values and a higher R-squared.The evaluation shows that prediction accuracy of the Random Forest model is better than that of the Multiple Linear Regression model.

关 键 词：多元线性回归 随机森林 房价预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] F224[自动化与计算机技术—计算机科学与技术]