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dc.contributor.advisorPiquero, Alex R.
dc.creatorOzkan, Turgut
dc.date.accessioned2017-06-13T21:13:23Z
dc.date.available2017-06-13T21:13:23Z
dc.date.created2017-05
dc.date.issued2017-05
dc.date.submittedMay 2017
dc.identifier.urihttp://hdl.handle.net/10735.1/5405
dc.description.abstractThe current study attempts to explore more precise solutions for the prediction problem in the U.S. criminal justice system. Using data obtained from the Bureau of Justice Statistics (BJS), Recidivism of Prisoners Released in 1994, this project investigates a number of statistical models in terms of their classification performances. Specifically, after a careful feature selection process, the conventional logistic regression model is compared to several machine learning models, including random forests, support vector machines, XGBoost, neural networks, and Search algorithm. According to the results, XGBoost and neural networks outperformed all other models in the comparison. Implications for the U.S. criminal justice system are discussed.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsCopyright ©2017 is held by the author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectRecidivism
dc.subjectMachine learning
dc.subjectCriminal statistics
dc.subjectQuantitative research
dc.subjectPrisons
dc.subjectCriminal behavior, Prediction of
dc.titlePredicting Recidivism Through Machine Learning
dc.typeDissertation
dc.date.updated2017-06-13T21:13:23Z
dc.type.materialtext
thesis.degree.grantorUniversity of Texas at Dallas
thesis.degree.departmentCriminology
thesis.degree.levelDoctoral
thesis.degree.namePHD


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