Estimation of MTPL claim frequency using GLM, GAM and XGBoost techniques
| dc.contributor.advisor | Käärik, Meelis, juhendaja | |
| dc.contributor.author | Puskar, Linnet | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Matemaatika ja statistika instituut | et |
| dc.date.accessioned | 2020-07-01T12:23:12Z | |
| dc.date.available | 2020-07-01T12:23:12Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The 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.uri | http://hdl.handle.net/10062/68242 | |
| dc.language.iso | eng | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | R (programmeerimiskeel) | et |
| dc.subject | R (programming language) | en |
| dc.subject.other | sõidukikindlustus | et |
| dc.subject.other | üldistatud lineaarsed mudelid | et |
| dc.subject.other | tehisõpe | et |
| dc.subject.other | Python (programmeerimiskeel) | et |
| dc.subject.other | motor vehicle insurance | en |
| dc.subject.other | generalized linear models | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | Python (programming language) | en |
| dc.title | Estimation of MTPL claim frequency using GLM, GAM and XGBoost techniques | en |
| dc.type | info:eu-repo/semantics/masterThesis | et |