Chapter 9 Data preprocessing

The following content is based on the tidymodels documentation and Alisson Hill’s tidymodels workshop.

In this tutorial, we’ll explore a tidymodels package, recipes, which is designed to help you preprocess your data before training your model.

Recipes are built as a series of preprocessing steps, such as:

• converting qualitative predictors to indicator variables (also known as dummy variables),
• transforming data to be on a different scale (e.g., taking the logarithm of a variable),
• transforming whole groups of predictors together,
• extracting key features from raw variables (e.g., getting the day of the week out of a date variable),

and so on. If you are familiar with R’s formula interface, a lot of this might sound familiar and like what a formula already does. Recipes can be used to do many of the same things, but they have a much wider range of possibilities. This article shows how to use recipes for modeling.

In summary, the idea of the recipes package is to define a recipe or blueprint that can be used to sequentially define the encodings and preprocessing of the data (i.e. “feature engineering”) before we build our models.