The R Journal: accepted article

This article will be copy edited and may be changed before publication.

auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics PDF download
Alicja Gosiewska and Przemysław Biecek

Abstract Machine learning models have spread to almost every area of life. They are successfully 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. The problem arises when models fail confronted with the real-world data. 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 identification of outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and consistent grammar, it is simple to validate models of any classes.

Received: 2018-12-01; online 2019-08-18, supplementary material, (581 bytes)
CRAN packages: auditor

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  author = {Alicja Gosiewska and Przemysław Biecek},
  title = {{auditor: an R Package for Model-Agnostic Visual Validation
          and Diagnostics}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-036},
  url = {}