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This article is cited in 1 scientific paper (total in 1 paper)
Scientific Part
Computer Sciences
Software implementation of ensemble models for the analysis of regional socio-economic development indicators
G. Yu. Chernyshova, N. D. Rasskazkin Saratov State University, 83 Astrakhanskaya St., Saratov 410012, Russia
Abstract:
To predict indicators, modern approaches based on machine learning are increasingly being used, as a result, additional tools appear for quantitatively assessing the level of development of socio-economic systems. One of the relevant approaches in machine learning is the use of ensemble methods. The purpose of this study is to develop an approach for processing panel data using special regression models, in particular, the ensembles. An application is presented to implement and compare various regression models, including GPBoost, for panel data used in regional statistics. The application was tested on the example of assessing the innovative potential of Russian regions.
Key words:
panel data, machine learning, boosting, decision tree, regional development.
Received: 24.11.2021 Accepted: 21.12.2021
Citation:
G. Yu. Chernyshova, N. D. Rasskazkin, “Software implementation of ensemble models for the analysis of regional socio-economic development indicators”, Izv. Saratov Univ. Math. Mech. Inform., 22:1 (2022), 130–137
Linking options:
https://www.mathnet.ru/eng/isu927 https://www.mathnet.ru/eng/isu/v22/i1/p130
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Abstract page: | 55 | Full-text PDF : | 49 | References: | 14 |
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