auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic

Machine learning has spread to almost every area of life. It is successfully applied in biology, medicine, finance, physics, and other fields. The problem arises if models fail when confronted with the real-world data. Therefore, there is a need for validation methods. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In addition, they may be used for analysis of the similarity of residuals and for the identification of outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods are implemented in the auditor package for R. Due to the flexible and consistent grammar, it is simple to validate models of any classes.