Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity.
Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners' well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of ea...
| Publicado en: | Journal of Sex Research Vol. 59; no. 2; pp. 224 - 238 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb 2022
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| 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=154901812&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 154901812 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00224499 SEX jtl: Journal of Sex Research issn: 00224499 maglogo: Y pubinfo: dt: Feb 2022 vid: 59 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 154901812 10.1080/00224499.2021.1967846 ppf: 224 ppct: 14 formats: tig: atl: Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity. aug: au: Vowels, Laura M. Vowels, Matthew J. Mark, Kristen P. affil: Department of Psychology, University of Lausanne Centre for Computer Vision, Speech and Signal Processing (CVSSP), University of Surrey Department of Family Medicine and Community Health, University of Minnesota su: Infidelity (Couples) Man-woman relationships Machine learning Random forest algorithms Effect sizes (Statistics) Decision trees sug: subj: Infidelity (Couples) Man-woman relationships Machine learning Random forest algorithms Effect sizes (Statistics) Decision trees ab: Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners' well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity across two studies (one individual and one dyadic, N = 1,295). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall and interpersonal factors such as relationship satisfaction, love, desire, and relationship length were the most predictive of online and in person infidelity. The results suggest that addressing relationship difficulties early in the relationship may help prevent infidelity. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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