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Technology of forecasting potentially unstable credit organizations based on machine learning methods

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dc.contributor.authorDomashova, J.
dc.contributor.authorKulaev, M.
dc.contributor.authorДомашова, Дженни Владимировна
dc.date.accessioned2024-11-26T13:11:26Z
dc.date.available2024-11-26T13:11:26Z
dc.date.issued2020
dc.description.abstract© 2020 The Authors. Published by Elsevier B.V.The article presents the results of the application of machine learning methods, in particular, various modifications of decision trees, to predict potentially unstable credit organizations. The application of different modifications of decision trees in the modeling of the specified task and current situation in banking sphere are considered. The technology for solving classification problems using machine learning methods is generalized. A Python program script, which enables to solve classification problems on the basis of the proposed methodology, was developed. The results of the application of machine learning methods using the developed program to solve this problem were described and their quality was analyzed.
dc.format.extentС. 767-772
dc.identifier.citationDomashova, J. Technology of forecasting potentially unstable credit organizations based on machine learning methods / Domashova, J., Kulaev, M. // Procedia Computer Science. - 2020. - 169. - P. 767-772. - 10.1016/j.procs.2020.02.167
dc.identifier.doi10.1016/j.procs.2020.02.167
dc.identifier.urihttps://www.doi.org/10.1016/j.procs.2020.02.167
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85084460157&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/21725
dc.relation.ispartofProcedia Computer Science
dc.titleTechnology of forecasting potentially unstable credit organizations based on machine learning methods
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume169
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