The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism.
The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied mac...
| Publicado en: | Criminal Justice & Behavior Vol. 48; no. 4; pp. 518 - 539 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Sage Publications Inc.
Apr2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=149337210&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149337210 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00938548 CJB jtl: Criminal Justice & Behavior issn: 00938548 maglogo: Y pubinfo: dt: Apr2021 vid: 48 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 149337210 10.1177/0093854820969753 ppf: 518 ppct: 21 formats: tig: atl: The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism. aug: au: Ghasemi, Mehdi Anvari, Daniel Atapour, Mahshid Stephen wormith, J. Stockdale, Keira C. Spiteri, Raymond J. affil: University of Saskatchewan Kwantlen Polytechnic University Capilano University Saskatoon Police Service University of Saskatchewan su: Ontario Recidivism Predictive validity Machine learning Receiver operating characteristic curves Random forest algorithms sug: subj: Recidivism Ontario Predictive validity Machine learning Receiver operating characteristic curves Random forest algorithms keyword: LS/CMI machine learning predictive accuracy risk–need assessment LS/CMI machine learning predictive accuracy risk–need assessment ab: The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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