<p>The house price prediction problem is a typical regression problem, and most of the common house price prediction models are single prediction algorithms, which are not ideal in terms of accuracy and stability. For solving this problem, this paper proposes a house price forecasting method based on Stacking-Sorted-Weighted-Ensemble (SSWE) model. Considering the characteristics of different algorithms and giving full play to the advantages of each model, multiple individual forecasting models are fused with the Stacking model. The algorithm validation is performed using the data generated by the system of real estate management department in western Guangdong. The prediction results show that the Stacking model is superior to the single model. Compared with the Stacking regression model, the SSWE model has a 13.6% increase in the root mean square error on the training set but a 0.3% decrease on the test set, indicating that the SSWE model prevents overfitting to a some extent and increases the accuracy and stability of the model.</p> <p> </p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.