Main Article Content
The measurement and reporting of model error is of basic importance when constructing models. Here, a general method and an R package, A3, are presented to support the assessment and communication of the quality of a model fit along with metrics of variable importance. The presented method is accurate, robust, and adaptable to a wide range of predictive modeling algorithms. The method is described along with case studies and a usage guide. It is shown how the method can be used to obtain more accurate models for prediction and how this may simultaneously lead to altered inferences and conclusions about the impact of potential drivers within a system.