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.
Andrew is a data reporter on the rapid-response investigative team at The Washington Post who has analyzed how covid-19 has disproportionately impacted certain communities, the spread of opioids across the country, and the rise of right-wing violence. He shared in winning the Pulitzer Prize for Investigative Reporting in 2018. He's an advocate for open data and reproducibility in journalism.
