Pipeable steps for feature engineering and data preprocessing to prepare for modeling
R
The recipes package provides a dplyr-like interface for building feature engineering pipelines to prepare data for modeling. It allows you to define a sequence of preprocessing steps that can be applied consistently across training and test datasets.
Recipes offers an alternative to R’s traditional formula and model.matrix approach, addressing their limitations when handling complex preprocessing workflows. The package excels at tasks like normalizing predictors, handling categorical variables, and creating derived features through a composable, step-by-step framework. It integrates seamlessly with the tidymodels ecosystem for end-to-end modeling workflows.
