Construction and application of innovative rating of regions using random forest technology

The published article is devoted to the urgent problem of developing models for constructing and applying an innovative rating of the country’s regions that have a radio-electronic industry (REP) branch. To overcome a number of shortcomings of known classical statistical approaches to improve the accuracy of forecasting the development of the REP industry and the adequacy of the ratings assigned to it. A version of the machine learning technology "random forest" has been developed and applied, increasing the accuracy of forecasting the development of the REP industry. The obtained ratings have been verified using data from a new observation period in order to determine the leading regions of the segments of the model’s input variables.

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Publication Details

Journal
Vestnik of Samara University Economics and Management
Published
2024-12-24
DOI
https://doi.org/10.18287/2542-0461-2024-15-4-187-201
Primary Topic
Analysis of environmental and stochastic processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Construction and application of innovative rating of regions using random forest technology

Nadezhda I. Yashina, Sergey N. Yashin, Egor Koshelev
Vestnik of Samara University Economics and Management
Analysis of environmental and stochastic processes
article

Construction and application of innovative rating of regions using random forest technology

Nadezhda I. Yashina, Sergey N. Yashin, Egor Koshelev
article en

Abstract

The published article is devoted to the urgent problem of developing models for constructing and applying an innovative rating of the country’s regions that have a radio-electronic industry (REP) branch. To overcome a number of shortcomings of known classical statistical approaches to improve the accuracy of forecasting the development of the REP industry and the adequacy of the ratings assigned to it. A version of the machine learning technology "random forest" has been developed and applied, increasing the accuracy of forecasting the development of the REP industry. The obtained ratings have been verified using data from a new observation period in order to determine the leading regions of the segments of the model’s input variables.

Vestnik of Samara University Economics and ManagementVol. 15(4)
N. I. Lobachevsky State University of Nizhny Novgorod (RU)
Russian Science Foundation
Openalex Percentile: Top 58%
Analysis of environmental and stochastic processes
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