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 software engineer at Posit PBC where he works on tidymodels, a collection of open-source R packages for statistical modeling. With an academic background in statistics and sociology, Simon believes that principled tooling has a profound impact on our ability to think intuitively and rigorously about data science.
