Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)

For citation:

Chernyshova G. Y., Rasskazkin N. D. Software implementation of ensemble models for the analysis of regional socio-economic development indicators. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2022, vol. 22, iss. 1, pp. 130-137. DOI: 10.18500/1816-9791-2022-22-1-130-137, EDN: XEKOIL

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.03.2022
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Language: 
English
Heading: 
Computer Sciences
Article type: 
Article
UDC: 
519.688
DOI: 
10.18500/1816-9791-2022-22-1-130-137
EDN: 
XEKOIL

Software implementation of ensemble models for the analysis of regional socio-economic development indicators

Autors: 
Chernyshova Galina Yu., Saratov State University
Rasskazkin Nikita D., Saratov State University
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
Acknowledgments: 
This work was supported by the Russian Foundation for Basic Research (project No. 20-010-00465).
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Received: 
24.11.2021
Accepted: 
21.12.2021
Published: 
31.03.2022
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