Estimation of MTPL claim frequency using GLM, GAM and XGBoost techniques

dc.contributor.advisorKäärik, Meelis, juhendaja
dc.contributor.authorPuskar, Linnet
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Matemaatika ja statistika instituutet
dc.date.accessioned2020-07-01T12:23:12Z
dc.date.available2020-07-01T12:23:12Z
dc.date.issued2020
dc.description.abstractThe purpose of this master’s thesis is to provide an overview of the XGBoost algorithm and examine its suitability to model the claim frequency of motor third party liability insurance. The first three chapters introduce generalized linear models, generalized additive models and the algorithms of gradient boosting and XGBoost. In the fourth chapter, the aforementioned methods are applied on the data of Estonian Motor Insurance Bureau to predict claim frequency.en
dc.identifier.urihttp://hdl.handle.net/10062/68242
dc.language.isoenget
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectR (programmeerimiskeel)et
dc.subjectR (programming language)en
dc.subject.othersõidukikindlustuset
dc.subject.otherüldistatud lineaarsed mudelidet
dc.subject.othertehisõpeet
dc.subject.otherPython (programmeerimiskeel)et
dc.subject.othermotor vehicle insuranceen
dc.subject.othergeneralized linear modelsen
dc.subject.othermachine learningen
dc.subject.otherPython (programming language)en
dc.titleEstimation of MTPL claim frequency using GLM, GAM and XGBoost techniquesen
dc.typeinfo:eu-repo/semantics/masterThesiset

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