Fairness and parameter importance in logistic regression models of criminal sentencing data

Bibliographic Details
Main Author:	Kaponen, Martina
Format:	Others
Language:	English
Published:	Uppsala universitet, Tillämpad matematik och statistik 2020
Subjects:	Other Mathematics Annan matematik
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417359

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spelling	ndltd-UPSALLA1-oai-DiVA.org-uu-4173592020-08-20T05:35:00ZFairness and parameter importance in logistic regression models of criminal sentencing dataengKaponen, MartinaUppsala universitet, Tillämpad matematik och statistik2020Other MathematicsAnnan matematikStudent thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417359U.U.D.M. project report ; 2020:26application/pdfinfo:eu-repo/semantics/openAccess
collection	NDLTD
language	English
format	Others
sources	NDLTD
topic	Other Mathematics Annan matematik
spellingShingle	Other Mathematics Annan matematik Kaponen, Martina Fairness and parameter importance in logistic regression models of criminal sentencing data
author	Kaponen, Martina
author_facet	Kaponen, Martina
author_sort	Kaponen, Martina
title	Fairness and parameter importance in logistic regression models of criminal sentencing data
title_short	Fairness and parameter importance in logistic regression models of criminal sentencing data
title_full	Fairness and parameter importance in logistic regression models of criminal sentencing data
title_fullStr	Fairness and parameter importance in logistic regression models of criminal sentencing data
title_full_unstemmed	Fairness and parameter importance in logistic regression models of criminal sentencing data
title_sort	fairness and parameter importance in logistic regression models of criminal sentencing data
publisher	Uppsala universitet, Tillämpad matematik och statistik
publishDate	2020
url	http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417359
work_keys_str_mv	AT kaponenmartina fairnessandparameterimportanceinlogisticregressionmodelsofcriminalsentencingdata
_version_	1719338501973999616

Fairness and parameter importance in logistic regression models of criminal sentencing data

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