A Real Estate Valuation Model Based on Random Forest and Light GBM
In order to solve the problems of single data source,idealization of influencing factors and over reliance on subjec-tive experience in Feature Engineering in commercial housing evaluation,a new method based on random forest and light GBM mod-el is proposed.RF_Lightgbm model is used to evaluate real estate value.Firstly,the importance of features is sorted by random for-est,and the features that have little influence on the real estate price are excluded.The grid search algorithm is used to optimize the model.Finally,the method is applied to the real estate value evaluation.Experiments on real house price data sets show that,com-pared with traditional models such as random forest and XGboost,RF is better.The accuracy of lightgbm model is improved by 1.7%,and the percentage error within 0%～10%accounts for 88.38%.It shows that the model can be well applied to the real estate value evaluation,and the evaluation results are more accurate.
random forestproperty appraisalcharacteristic engineeringLight GBM
国家科技期刊平台
NSTL
万方数据
