{"id":{"repo_id":"vilnius","oai_identifier":"oai:vu.lt:elaba:81807179"},"canonical_url":"https://search.dev.ndltd.org/etd/vilnius/oai:vu.lt:elaba:81807179","repository":{"repo_id":"vilnius","name":"Vilnius University","base_url":"https://epublications.vu.lt/oai"},"display":{"title":"Kredito rizikos prognozavimas naudojantis mašininio mokymosi algoritmu ,,XGboost\" /","abstract":"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. 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