Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism

Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism

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Bibliographic Details
Main Author: Aaltonen, Olli-Pekka
Other Authors: Faculty of Information Technology, Informaatioteknologian tiedekunta, Tietojenkäsittelytieteiden laitos, Department of Computer Science and Information Systems, University of Jyväskylä, Jyväskylän yliopisto, Information Systems Science, Tietojärjestelmätiede, 601
Format: Master's thesis
Language:eng
Published: 2016
Subjects:
Recidivism
machine learning
Random forest
logistic regression
forecasting
uusintarikollisuus
ennusteet
regressioanalyysi
koneoppiminen
Online Access: https://jyx.jyu.fi/handle/123456789/51967
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annif_keywords_txtF_mv recidivism regression analysis forecasts machine learning forests
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author Aaltonen, Olli-Pekka
author2 Faculty of Information Technology Informaatioteknologian tiedekunta Tietojenkäsittelytieteiden laitos Department of Computer Science and Information Systems University of Jyväskylä Jyväskylän yliopisto Information Systems Science Tietojärjestelmätiede 601
author_facet Aaltonen, Olli-Pekka Faculty of Information Technology Informaatioteknologian tiedekunta Tietojenkäsittelytieteiden laitos Department of Computer Science and Information Systems University of Jyväskylä Jyväskylän yliopisto Information Systems Science Tietojärjestelmätiede 601 Aaltonen, Olli-Pekka
author_sort Aaltonen, Olli-Pekka
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department_txtF Informaatioteknologia
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format_ext_str_mv Opinnäyte Maisterivaiheen työ
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spellingShingle Aaltonen, Olli-Pekka Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism Recidivism machine learning Random forest logistic regression forecasting uusintarikollisuus ennusteet regressioanalyysi koneoppiminen
subject_txtF Kognitiotiede
thumbnail https://jyu.finna.fi/Cover/Show?source=Solr&id=jyx.123456789_51967&index=0&size=large
title Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_full Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_fullStr Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_full_unstemmed Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_short Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_sort comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
title_txtP Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
topic Recidivism machine learning Random forest logistic regression forecasting uusintarikollisuus ennusteet regressioanalyysi koneoppiminen
topic_facet Random forest Recidivism ennusteet forecasting koneoppiminen logistic regression machine learning regressioanalyysi uusintarikollisuus
url https://jyx.jyu.fi/handle/123456789/51967 http://www.urn.fi/URN:NBN:fi:jyu-201611234724
work_keys_str_mv AT aaltonenollipekka comparingtheforecastingperformanceoflogisticregressionandrandomforestmodelsinc

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