How to build and deploy a basic machine learning model using the tidymodels framework in R?

All answers to this question.

tidymodels is essentially the "tidyverse" version of caret. It breaks the process into distinct packages: rsample for splitting, recipes for preprocessing, parsnip for the model interface, and yardstick for evaluation. The biggest advantage is consistency. In caret, different models often had different parameter names, but in tidymodels, the interface is unified. For a random forest, you’d define the model once with rand_forest() and can then easily swap it out for a different engine like ranger or randomForest without rewriting your entire preprocessing and evaluation code.

   Answered 2025-03-04 by Barbara Taylor

Do you find the pipe-based syntax of tidymodels more intuitive for your current workflow, or do you prefer the more consolidated, single-function approach of the older caret package?

   Answered 2025-03-07 by Richard Moore

• Richard, I actually love the pipe syntax! It makes the data flow so much clearer. With tidymodels, I feel like I have more control over the feature engineering steps in the recipes phase. It feels less like a "black box" than caret did. I'm especially excited about the workflows package, which bundles the recipe and model together, making it much easier to manage my deployment scripts without losing track of which preprocessing steps go with which model.

     Commented 2025-02-10 by William Jones

One thing to note is that tidymodels has a steeper learning curve because there are more pieces to manage, but it scales much better for complex projects.

   Answered 2025-03-13 by Susan Clark

• I agree with Susan. It takes a bit longer to set up initially, but the modularity means you can debug individual parts of your pipeline much more easily than in caret.

     Commented 2025-03-15 by James Wilson