Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole.
Simon Couch is a member of the AI Core Team at Posit, working at the intersection of R and LLMs. He’s authored several packages that help R users get more out of LLMs, from package-based assistants to tools for evaluation to implementations of emerging technologies like the Model Context Protocol. Drawing on his background in statistics, Simon worked on the tidymodels framework for machine learning in R for a number of years before transitioning to working on LLMs.
