Artikel ; Online: Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.
2022 Band 41, Seite(n) 32–45
Abstract: Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items ... ...
| Abstract | Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R |
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| Mesh-Begriff(e) | Body Image/psychology ; Humans ; Linear Models ; Machine Learning | |||||
| Sprache | Englisch | |||||
| Erscheinungsdatum | 2022-02-25 | |||||
| Erscheinungsland | Netherlands | |||||
| Dokumenttyp | Journal Article | |||||
| ZDB-ID | 2211449-X | |||||
| ISSN | 1873-6807 ; 1740-1445 | |||||
| ISSN (online) | 1873-6807 | |||||
| ISSN | 1740-1445 | |||||
| DOI | 10.1016/j.bodyim.2022.01.013 | |||||
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| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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