auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
This provides a tool for researchers and practitioners in fields like biology and finance to validate models beyond standard accuracy metrics, though it is incremental as it builds on existing diagnostic concepts.
The paper tackles the problem of machine learning models failing on real-world data by introducing a model-agnostic audit methodology and tools for visual validation and diagnostics, implemented in the auditor R package to assess goodness of fit, compare performance, analyze residuals, and identify outliers.
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 the test set. The problem arises when 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 fit and performance of models. In~addition, they may be used for the 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.