Machine learning with {tidymodels}


Summary: This workshop will provide a gentle introduction to machine learning with R using the modern suite of predictive modeling packages called tidymodels. We will build, evaluate, compare, and tune predictive models. Along the way, we’ll learn about key concepts in machine learning including overfitting, the holdout method, the bias-variance trade-off, ensembling, cross-validation, and feature engineering. Learners will gain knowledge about good predictive modeling practices, as well as hands-on experience using tidymodels packages like parsnip, rsample, recipes, yardstick, and tune.

Pre-requisites: Some introductory experience with R.


Please join the workshop with a computer that has the following installed (all available for free):

A recent version of R, available at A recent version of RStudio Desktop (RStudio Desktop Open Source License, at least v2022.02), available at The following R packages, which you can install from the R console:

install.packages(c("embed", "forcats","remotes", 
                   "tidymodels", "glmnet"))




Link to lab for local download here