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Machine learning models have spread to almost every area of life. They aresuccessfully applied in biology, medicine, finance, physics, and other fields.With modern software it is easy to train even a~complex model that fits thetraining data and results in high accuracy on the test set. The problem ariseswhen models fail confronted with real-world data.  This paper describes methodology and tools for model-agnostic audit.Introduced techniques facilitate assessing and comparing the goodness of fitand performance of models. In~addition, they may be used for the analysis ofthe similarity of residuals and for identification of~outliers and influentialobservations. The examination is carried out by diagnostic scores and visualverification.  Presented methods were implemented in the auditor package for R. Due toflexible and~consistent grammar, it is simple to validate models of anyclasses.

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