A couple small changes can greatly speed up the hyperparameter tuning process with tidymodels.

Recent optimizations have made fits on small datasets much, much snappier.

Some short reflections on working on the {infer} R package.

On the tension between documenting R packages exhaustively and maintainably.

Weighing the pros and cons of several possible schemas for naming the core functions in {stacks}.

Why {stacks} requires (at least) four separate functions to build an ensemble model rather than wrapping them all up into one.

Introducing a set of blog posts reflecting on the development process of the {stacks} package.

Introducing ensemble learning to the tidymodels.

Model stacking is an ensembling technique that involves training a model to combine the outputs of many diverse statistical models. The stacks package implements a grammar for tidymodels-aligned model stacking.