A few simple rules for prediction

Prediction Rule Ensembling (PRE) is a statistical learning method that aims to balance predictive accuracy and interpretability. It inherits high predictive accuracy from decision tree ensembles (e.g., random forests, boosted tree ensembles) and high interpretability from sparse regression methods and single decision trees. In this presentation, I will introduce PRE methodology, starting from the algorithm originally proposed by Friedman and Popescu (2008). I will show several real-data applications, for example on the prediction of academic achievement and chronic depression. I will discuss several useful extensions of the original algorithm which are already implemented in R package ‘pre’, like the inclusion of a-priori knowledge, unbiased rule derivation, and (non-)negativity constraints. Finally, I will discuss current work in which we leverage the predictive power of black-box models (e.g., Bayesian additive regression trees, deep learning) to further improve accuracy and interpretability of PRE.

Already curious? Here is an introduction to the method and R package: https://github.com/marjoleinF/pre#readme. Here are the tutorial and technical/theoretical papers:

Fokkema, M., & Strobl, C. (2020). Fitting prediction rule ensembles to psychological research data: An introduction and tutorial. Psychological Methods, 25(5), 636–652. https://arxiv.org/abs/1907.05302

Fokkema, M. (2020). Fitting prediction rule ensembles with R package pre. Journal of Statistical Software, 92(1), 1-30. https://doi.org/10.18637/jss.v092.i12

Fokkema, M., Smits, N., Kelderman, H., & Penninx, B. W. J. H. (2015). Connecting clinical and actuarial prediction with rule-based methods. Psychological Assessment, 27(2), 636–644. https://pure.uva.nl/ws/files/53367537/2704922.pdf

Meeting link

Please contact Chris Flinterman for the meeting link.