基于 LRF 方法的在线短租房源价格特征选择研究

为解决单一特征选择方法的局限性问题，提出 Lasso-RF（LRF）混合特征选择方法，并应用于在线短租房源价格问题研究。基于 Airbnb 房源数据，实验首先通过 Lasso 回归进行特征选择，处理特征之间的多重共线性|然后采用随机森林算法精选剩余特征，最终得到 35 个重要特征，并带入 4 个预测模型中进行比较。结果表明，特征之间的多重共线性会影响随机森林算法对特征重要度的度量|LRF-RF 预测模型与 RF-RF 预测模型相比，评价指标 R2 和 MSE 分别提高了 0.005、0.006，同时运行时间缩短 0.267 秒，表明 LRF 混合特征选择方法优于单一的 RF 特征选择方法。

Research on the Listings Price of Home-sharing Accommodation Based on Lasso and Random Forest

To solve the problem of the limitation of single feature selection method，a mixed feature selection method for Lasso-RF（LRF）is proposed and applied to the listings price of home-sharing accommodation. Based on the data of Airbnb，the experiment does the feature selection by Lasso regression firstly，dealing with the multicollinearity between features. Then the experiment selects the residual features by Random forest. Finally，35 important features are selected out and used in four prediction models in order to evaluate and compare the results. The results show that the multicollinearity between the features will affect the measurement of the importance of the random forest to the features. Comparison between LRF-RF prediction model and RF-RF prediction model shows that evaluation indexes R2 and MSE was increased by 0.005 and 0.006 respectively，and the running time was reduced by 0.267 seconds.The evaluation result show that LRF hybrid feature selection method is better than single RF feature selection method.