The Predictive Five: A supervised learning approach to personality psychology

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Current research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM has been criticized for its relatively limited predictive ability. We propose a novel approach to modeling human personality that is based on the maximization of the model’s predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized supervised machine learning techniques for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five dimensional personality summary, which we term the Predictive Five (PF), provides predictive performance that is superior to the FFM in independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. Furthermore, we examine the between-participants’ replicability of the PF representation and show that the PF has good test-retest reliability, and as such provides an important addition to the psychologists' toolbox. The approach described herein has the promise of providing an interpretable low-dimensional personality representation, which is also predictive of behaviour.