Using machine learning to determine the shared and unique risk factors for marijuana use among child...
Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents
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Public Library of Science (PLoS)
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English
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Public Library of Science (PLoS)
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Objective This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Method Data were from a Time 4 (Mage = 18.22) of longitudinal study of the eff...
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Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents
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TN_cdi_doaj_primary_oai_doaj_org_article_c9445a5d4260417aad00c6716c95d0cb
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c9445a5d4260417aad00c6716c95d0cb
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E-ISSN
1932-6203
