Everything But The Model: Infrastructure for Assessing Spatial Models in R
Assessing spatial models can be difficult. Model errors may exhibit spatial autocorrelation, model predictions are often aggregated to multiple spatial scales by users, and models are often used to extrapolate outside the boundaries of their training data. To adjust for these considerations, modelers must choose from a dizzying array of assessment protocols and metrics which may give different or even contradictory results. Researchers are also often responsible for implementing these techniques themselves, or otherwise welding together multiple packages with incompatible interfaces in order to properly assess their models.
New tools for R’s tidymodels modeling framework aim to reduce this complexity, implementing common model assessment tasks in a straightforward, computationally efficient, and easy-to-learn manner.