Machine Learning and Risk Assessment: Random Forest Does Not Outperform Logistic Regression in the Prediction of Sexual Recidivism.
Although many studies supported the use of actuarial risk assessment instruments (ARAIs) because they outperformed unstructured judgments, it remains an ongoing challenge to seek potentials for improvement of their predictive performance. Machine learning (ML) algorithms, like random forests, are ab...
| Published in: | Assessment Vol. 31; no. 2; pp. 460 - 482 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
Mar2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175032858&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175032858 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10731911 G70 jtl: Assessment issn: 10731911 maglogo: Y pubinfo: dt: Mar2024 vid: 31 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 175032858 163032421 175032858 175032858 10.1177/10731911231164624 175032858 ppf: 460 ppct: 22 formats: tig: atl: Machine Learning and Risk Assessment: Random Forest Does Not Outperform Logistic Regression in the Prediction of Sexual Recidivism. aug: au: Etzler, Sonja Schönbrodt, Felix D. Pargent, Florian Eher, Reinhard Rettenberger, Martin affil: Goethe-University Frankfurt am Main, Germany sug: subj: Machine Learning Recidivism Risk Factors Sexual Abuse Risk Factors Risk Assessment Human Male Austria Prospective Studies Random Forest Logistic Regression Algorithms Machine Learning Algorithms Descriptive Statistics Sex Offenders Predictive Validity Male ab: Although many studies supported the use of actuarial risk assessment instruments (ARAIs) because they outperformed unstructured judgments, it remains an ongoing challenge to seek potentials for improvement of their predictive performance. Machine learning (ML) algorithms, like random forests, are able to detect patterns in data useful for prediction purposes without explicitly programming them (e.g., by considering nonlinear effects between risk factors and the criterion). Therefore, the current study aims to compare conventional logistic regression analyses with the random forest algorithm on a sample of N = 511 adult male individuals convicted of sexual offenses. Data were collected at the Federal Evaluation Center for Violent and Sexual Offenders in Austria within a prospective-longitudinal research design and participants were followed-up for an average of M = 8.2 years. The Static-99, containing static risk factors, and the Stable-2007, containing stable dynamic risk factors, were included as predictors. The results demonstrated no superior predictive performance of the random forest compared with logistic regression; furthermore, methods of interpretable ML did not point to any robust nonlinear effects. Altogether, results supported the statistical use of logistic regression for the development and clinical application of ARAIs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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