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[{"key": "dc.contributor.advisor", "value": "Veijalainen, Jari", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Aaltonen, Olli-Pekka", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2016-11-23T08:10:17Z", "language": "", "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2016-11-23T08:10:17Z", "language": "", "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2016", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.other", "value": "oai:jykdok.linneanet.fi:1643461", "language": null, "element": "identifier", "qualifier": "other", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/51967", "language": "", "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Rikosseuraamusalalla on viime vuosina kehitetty uusintarikollisuutta ennustavia malleja (Tyni, 2015), jotka perustuvat tyypillisesti rekisteripohjaisiin mittareihin, jotka mittaavat mm. tuomitun sukupuolta, ik\u00e4\u00e4, rikostaustaa ja vankikertaisuutta. Yleens\u00e4 t\u00e4llaisten mallien kehityksess\u00e4 k\u00e4ytet\u00e4\u00e4n logistisen regressioanalyysin kaltaisia parametrisia malleja, joissa uusintarikollisuuden todenn\u00e4k\u00f6isyytt\u00e4 mallinnetaan taustamuuttujien lineaarisena funktiona. N\u00e4iden mallien rinnalle on viime aikoina kehitetty koneoppimisalgoritmeihin perustuvia vaihtoehtoja, joiden on todettu suoriutuvan k\u00e4yt\u00e4nn\u00f6n sovelluksissa uusintarikollisuuden ennustamisessa perinteisi\u00e4 malleja paremmin (Berk & Bleich, 2014). T\u00e4llaisten mallien toimivuutta suhteessa perinteisiin malleihin ei ole kuitenkaan testattu suomalaisella datalla. Tutkielman tarkoituksena on tarkastella sit\u00e4, kuinka hyvin erilaiset ennustemallit onnistuvat teht\u00e4v\u00e4ss\u00e4\u00e4n. Tutkielman ensimm\u00e4isess\u00e4 vaiheessa luodaan logistiseen regressioanalyysiin ja koneoppimisalgoritmiin (Random forest) perustuvat uusintarikollisuutta ennustavat mallit Kriminologian ja oikeuspolitiikan instituutin Rikosten ja seuraamusten tutkimusrekisterist\u00e4 poimitulla aineistolla, joka sis\u00e4lt\u00e4\u00e4 referenssituomioita vuosilta 2005-2007. Tuomituille henkil\u00f6ille on haettu tietoa my\u00f6s referenssituomiota edelt\u00e4v\u00e4st\u00e4 ja seuraavasta rikosk\u00e4ytt\u00e4ytymisest\u00e4. Ennustemalli luodaan vuosien 2005\u20132006 v\u00e4lill\u00e4 tuomittujen aineistolla, ja ennustemallia testataan vuoden 2007 datalla. N\u00e4in simuloidaan tilannetta, jossa havaittuun aineistoon perustuvalla historiallisella toteumatiedolla ennustetaan uuden tuomittujen ryhm\u00e4n viel\u00e4 toteutumatonta uusintarikollisuutta. Tutkimuskysymyksen\u00e4 kysyt\u00e4\u00e4nkin, kumpi malleista pystyy luomaan rikoshistoriatiedon perusteella paremman ennustusmallin. Molemmat mallit ennustavat uusinta-rikollisuutta tutkielman asetelmassa verrattain hyvin. Kumpikaan ennustemalli ei kuitenkaan ole toista parempi, sill\u00e4 menetelm\u00e4t tuottavat ennustustehokkuudeltaan varsin samantasoiset mallit. Tutkielman tuloksena todetaan, ettei Random forest \u2013koneoppimismenetelm\u00e4n ja logistisen regressiomallin ennustustehokkuuden v\u00e4lille saada merkitt\u00e4v\u00e4\u00e4 eroa tutkielman asetelmalla.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "During the recent years, predictive models have been created to predict the future criminal behavior (recidivism) of past offenders (e.g. Tyni, 2015). Predictive models are often created by using register-based indicators, e.g. offender\u2019s gender, age, criminal background, or prior imprisonments. Usually, these predictive models are created by using parametric models, where the likelihood of recidivating is modelled as a linear function of independent variables. Lately, machine learning algorithms have been introduced as alternatives to these more traditional models. In a recent American study, machine learning algorithms were stated to be more accurate predictors of recidivism than the more traditional logistic regression model (Berk & Bleich, 2014). However, these machine learning algorithms have not been tested for criminal recidivism prediction utilizing Finnish data. The aim of this thesis is to examine the comparative effectiveness of different risk prediction models in a Finnish setting. In this thesis, two predictive models for recidivism are created, one being a logistic regression model, and the other a machine learning algorithm-based model called Random forest. Research data was gathered from the RST (Rikosten ja seuraamusten tutkimusrekisteri, which translates to \u201cthe research register of crimes and sanctions\u201d) database of Institute of Criminology and Legal Policy, and includes all offenders convicted to several common crime type offenses in Finland from 2005 to 2007. Data also includes information on past and future criminal behavior for those offenders. Predictive models are developed with data from the years 2005 and 2006. The model testing is done with the remaining 2007 data, in order to simulate a situation where predictive models are used to predict recidivism yet to be actualized. The research question asks which of these models perform better in forecasting the criminal recidivism of a previous offender. The results of this study show that both logistic regression and Random forest algorithm create decent predictive models, but neither model outperforms the other on chosen performance metrics. The outcome, and the answer to the research question is, that neither model is better than the other in predicting recidivism among convicted offenders in Finland.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted using Plone Publishing form by Olli-Pekka Aaltonen (olaalton) on 2016-11-23 08:10:17.062676. Form: Pro gradu -lomake (https://kirjasto.jyu.fi/julkaisut/julkaisulomakkeet/pro-gradu-lomake). JyX data: [jyx_publishing-allowed (fi) =False]", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2016-11-23T08:10:17Z\r\nNo. of bitstreams: 2\r\nURN:NBN:fi:jyu-201611234724.pdf: 912639 bytes, checksum: 8de7cf2a706d8694d423ee1027ee65eb (MD5)\r\nlicense.html: 1183 bytes, checksum: 9f694debb5b3d5bee5941d30880e4965 (MD5)", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2016-11-23T08:10:17Z (GMT). No. of bitstreams: 2\r\nURN:NBN:fi:jyu-201611234724.pdf: 912639 bytes, checksum: 8de7cf2a706d8694d423ee1027ee65eb (MD5)\r\nlicense.html: 1183 bytes, checksum: 9f694debb5b3d5bee5941d30880e4965 (MD5)\r\n Previous issue date: 2016", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "1 verkkoaineisto (53 sivua)", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "Recidivism", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "machine learning", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "Random forest", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "logistic regression", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "forecasting", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201611234724", "language": null, "element": "identifier", "qualifier": "urn", 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