  There is a vast literature on the determinants of subjective wellbeing.
International organisations and statistical offices are now collecting such
survey data at scale. However, standard regression models explain surprisingly
little of the variation in wellbeing, limiting our ability to predict it. In
response, we here assess the potential of Machine Learning (ML) to help us
better understand wellbeing. We analyse wellbeing data on over a million
respondents from Germany, the UK, and the United States. In terms of predictive
power, our ML approaches do perform better than traditional models. Although
the size of the improvement is small in absolute terms, it turns out to be
substantial when compared to that of key variables like health. We moreover
find that drastically expanding the set of explanatory variables doubles the
predictive power of both OLS and the ML approaches on unseen data. The
variables identified as important by our ML algorithms - $i.e.$ material
conditions, health, and meaningful social relations - are similar to those that
have already been identified in the literature. In that sense, our data-driven
ML results validate the findings from conventional approaches.
