Applying the Tidymodels Framework for Spatial Machine Learning in R

This blog post explores how to implement spatial machine learning using the Tidymodels framework in R, a collection of packages built on tidyverse principles. It walks through the entire workflow—from preparing geospatial data using `terra` and `sf`, to building and tuning random forest models with `parsnip` and `ranger`. The post covers model training, spatial prediction using raster data, and two types of cross-validation: standard k-fold and spatial block. Key metrics like RMSE and R-squared are used to compare model performance. Furthermore, it demonstrates hyperparameter tuning with `tune_grid()` and visualization of results using `ggplot2`. The authors also introduce the `waywiser` package to evaluate model applicability across geographic space. This advanced tutorial is especially useful for data scientists and GIS analysts working with environmental or spatial datasets who want to move beyond traditional machine learning workflows and leverage a modular, extensible system. It concludes by emphasizing that while Tidymodels is powerful and flexible, it requires understanding multiple interdependent packages, making it better suited for users with intermediate to advanced R experience.