Using Decision Trees to Identify Salient Predictors of Cannabis-Related Outcomes.

Schwebel, Frank J; Richards, Dylan K; Pfund, Rory A; Joseph, Verlin W; Pearson, Matthew R

Schwebel, Frank J; Richards, Dylan K; Pfund, Rory A; Joseph, Verlin W; Pearson, Matthew R.

Afiliação

Schwebel FJ; Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA.
Richards DK; Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA.
Pfund RA; Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA.
Joseph VW; Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA.
Pearson MR; Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA.

J Psychoactive Drugs ; 54(5): 419-428, 2022.

Article em En | MEDLINE | ID: mdl-35067209

Assuntos

Cannabis; Abuso de Maconha; Humanos; Universidades; Árvores de Decisões

Palavras-chave

Machine learning; cannabis; college students; decision tree; recursive partitioning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cannabis / Abuso de Maconha Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Psychoactive Drugs Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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