四川大学学报（自然科学版）2026，Vol.63Issue(1)：218-223,6.DOI:10.19907/j.0490-6756.240191
一种基于Stacking集成机器学习的城市房租预测模型
A stacking ensemble machine learning model for the prediction of urban housing rental prices
摘要
Abstract
Effective and accurate prediction models for urban housing rental prices play an important role for the government to make relative policies.In this paper,we propose a stacking ensemble machine learning model to improve the efficiency and accuracy superior to the existed models.Based on the rental-price data of Chengdu during 2022 and 2023,we firstly show that the stacking model is the most accurate among six ordi-nary machine learning models and their stacking model,while the stacking model has the lowest time effi-ciency simultaneously.Then we construct a new stacking model by selecting the XGBoost and RF models as the first layer base learners,which greatly improves the time efficiency of the stacking model.Finally,empiri-cal analysis verifies the high accuracy and time efficiency of the new stacking model.关键词
住房租赁/房租预测/机器学习/Stacking集成Key words
housing rental/rent prediction/machine learning/stacking ensemble分类
数理科学引用本文复制引用
林靖宇,夏怡凡,张红历,陈凯伦,方疏桐..一种基于Stacking集成机器学习的城市房租预测模型[J].四川大学学报（自然科学版）,2026,63(1):218-223,6.基金项目
国家社科基金重大项目(22&ZD161) （22&ZD161）
成都市房屋租赁服务中心项目(2023110029) （2023110029）
