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dc.contributor.advisorLember, Jüri, juhendaja
dc.contributor.advisorGimbutas, Mark, juhendaja
dc.contributor.authorSharma, Priyush Protim
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Matemaatika ja statistika instituutet
dc.date.accessioned2021-08-25T06:55:18Z
dc.date.available2021-08-25T06:55:18Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/10062/73370
dc.description.abstractThe main aim of this master’s thesis work is to provide an overview of some tree-based models and to test the suitability of these models in finding the incorrectly submitted invoices received by the Estonian Health Insurance Fund. C4.5, CART and bagged CART are the three algorithms that are used to train the models and to apply binary classification with these models in order to reduce the number of invoices that must be checked manually.en
dc.language.isoenget
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEesti Haigekassaet
dc.subjectpuupõhised mudelidet
dc.subjectR (programmeerimiskeel)et
dc.subjectEstonian Health Insurance Funden
dc.subjecttree based modelsen
dc.subjectR (programming language)en
dc.subject.othermasinõpeet
dc.subject.othertehisõpeet
dc.subject.othermachine learningen
dc.subject.otherstatistical learningen
dc.titleTree-based methods in supervised learning with Estonian Health Insurance Fund dataen
dc.typeinfo:eu-repo/semantics/masterThesiset


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