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A Model Average algorithm for Housing Price Forecast with Evaluation Interpretation

Journal of Quantum Computing 2022年第3期4卷 147-163页

作者： Jintao Fu Yong Zhou Qian Qiu Guangwei Xu Neng Wan School of Computer Science Southwest Petroleum UniversityChengdu610500China Key Laboratory on Aero-Engine Altitude Simulation Technology Sichuan Gas Turbine EstablishmentAECCMianyang621000China Aero Engine Academy of China Aero Engine Corporation of ChinaBeijing101300China

In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in *** are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home *** regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing *** is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price *** today’s forecasting studies,ensemble learning is a very prevalent and well-liked study *** regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse *** Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate *** Average Model has a strong applicability in the field of regression prediction and significantly increases computational *** technique is also easier to replicate and very effective in regression *** using regression processing techniques,this work creates an average of different regression models using the am(Average Model)algorithm in a novel *** evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price *** experimental results show that the am algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accurac

关键词： Machine learning am algorithm price forecast regression algorithm Model evaluation

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