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Supervised machine learning methods in psychology: A practical introduction with annotated R code
Rosenbusch,Hannes Soldner,Felix Evans,Anthony M. Zeelenberg,Marcel
Rosenbusch,Hannes
Soldner,Felix
Evans,Anthony M.
Zeelenberg,Marcel
Abstract
Machine learning methods for prediction and pattern detection are increasingly prevalent in psychological research. We provide an introductory overview of machine learning, its applications, and describe how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize standard prediction algorithms including linear regressions, ridge regressions, decision trees, and random forests (plus additional algorithms in the supplementary materials). We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science.
Description
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
ACCURATE, BEHAVIOR, DEPRESSION, PERSONALITY, PREDICTION, REGRESSION, RISK, SELECTION
Citation
Rosenbusch, H, Soldner, F, Evans, A M & Zeelenberg, M 2021, 'Supervised machine learning methods in psychology : A practical introduction with annotated R code', Social and Personality Psychology Compass, vol. 15, no. 2, e12579. https://doi.org/10.1111/spc3.12579