Kredito rizikos prognozavimas naudojantis mašininio mokymosi algoritmu ,,XGboost" /

In this bachelor thesis I will introduce machine learning algorithm „XGboost” which will try to predict rating downgrade. Attention will be drawn to algorithm’s theoretical background, analysing how regression and regularization functions behave in tree space. Thus, methods for analysing model performence will be introduced and explained. From practical perspective, will try to set up explained algorithm for given prediction problem and solve it with use of „Python” libraries, created for statistics and machine learning. Though regression trees is not a new subject, nevertheless, with increased amount of data and development of computer technologies, capabilities of solving different problems have widened from medicine drugs testing to implementing an analytical checkers program.

Grantor dc:publisher

Institutional Repository of Vilnius University

Year dc:date

2020

Author dc:creator

• Norkus, Domantas,

Contributors dc:contributor

• Manstavičius, Martynas

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

lit

Repository record dc:identifier

https://repository.vu.lt/VU:ELABAETD81807179&prefLang=en_US

OAI identifier oai:identifier

oai:vu.lt:elaba:81807179