Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The US Body Project I
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 (eg …
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 (eg …
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