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

Authors

Alicja Gosiewska, Warsaw University of Technology
Przemysław Biecek, Warsaw University of Technology

Date of this Version

11-2019

Document Type

Article

Citation

The R Journal (December 2019) 11(2); Editor: Michael J. Kane

Comments

Copyright 2019, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Machine learning models have successfully been applied to challenges in applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a complex model that fits the training data and results in high accuracy on test set. However, problems often arise when models are confronted with the real-world data. This paper describes methodology and tools for model-agnostic auditing. It provides functinos for assessing and comparing the goodness of fit and performance of models. In addition, the package may be used for analysis of the similarity of residuals and for identification of outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. The code presented in this paper are implemented in the auditor package. Its flexible and consistent grammar facilitates the validation models of a large class of models.