基于集成学习的房租预测研究

Research of Prediction on House Rent Based on Intergration Learning

住房租赁市场的快速发展使得人们对房屋租赁信息的需求不断增加,对房屋租金关注持续变高。租房市场供给两端一直存在着信息不对称的问题,房租是由诸多方面因素共同决定的,而现有的基于单一算法的房租预测模型,其预测精度往往受模型性能好坏、噪声、以及过度拟合风险等因素影响。本文基于堆叠集成策略,融合Random Forest Regressor、Extra Trees Regressor、LightGBM三个基模型,建立了集成学习的房租预测模型。研究结果表明,本方法预测精度明显优于任一单一预测模型,提高了预测的准确性和稳定性,证实了该模型在房租预测上的有效性。

The rapid development of the housing rental market has led to an increasing demand for housing rental information. There is always a problem of information asymmetry at both ends of the rental market. The rent is determined by many factors together. Accuracy of a single prediction model is unstable and is often affected by factors such as model performance, noise, and over-fitting risk. This study aims to develop and evaluate models of rental market dynamics using stacking integra-tion strategy on data from the DC competition community. We use the three basic models of Ran-dom Force Regressor, Extra Trees Regressor and LightGBM and establish a rent prediction model for integrated learning. The experimental results show that the prediction accuracy of this method is obviously better than any single prediction model, which improves the accuracy and stability of the prediction, and confirms the validity of the model in rent prediction.