Review of Different Methods of Embedded Feature Selection in Machine Learning Models for Genetic Risk Prediction

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorWharrie, Sophie
dc.contributor.authorTaskin, Selin
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorKorpi-Lagg, Maarit
dc.date.accessioned2023-05-30T08:10:39Z
dc.date.available2023-05-30T08:10:39Z
dc.date.issued2023-04-28
dc.format.extent30+6
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/121107
dc.identifier.urnURN:NBN:fi:aalto-202305303441
dc.language.isoenen
dc.programmeAalto Bachelor’s Programme in Science and Technologyfi
dc.programme.majorData Scienceen
dc.programme.mcodeSCI3095fi
dc.subject.keywordgenetic risk scoringen
dc.subject.keywordmachine learningen
dc.subject.keywordfeature selectionen
dc.subject.keywordlassoen
dc.subject.keywordrandom foresten
dc.titleReview of Different Methods of Embedded Feature Selection in Machine Learning Models for Genetic Risk Predictionen
dc.typeG1 Kandidaatintyöfi
dc.type.dcmitypetexten
dc.type.ontasotBachelor's thesisen
dc.type.ontasotKandidaatintyöfi

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