Rosenbusch, H;
Soldner, F;
Evans, AM;
Zeelenberg, M;
(2021)
Supervised machine learning methods in psychology: A practical introduction with annotated R code.
Social and Personality Psychology Compass
, 15
(2)
, Article e12579. 10.1111/spc3.12579.
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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.
| Type: | Article |
|---|---|
| Title: | Supervised machine learning methods in psychology: A practical introduction with annotated R code |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/spc3.12579 |
| Publisher version: | https://doi.org/10.1111/spc3.12579 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Keywords: | Social Sciences, Psychology, Social, Psychology, DEPRESSION, REGRESSION, PERSONALITY, PREDICTION, SELECTION, ACCURATE, BEHAVIOR, RISK |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10140439 |
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