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

TL;DR

This paper introduces the auditor R package, which provides model-agnostic tools for visual validation, diagnostics, and performance assessment of machine learning models across various fields.

Contribution

It presents a flexible, model-agnostic methodology and implements it in an R package for comprehensive model validation and diagnostics.

Findings

Enables assessment of model goodness of fit and performance.

Facilitates analysis of residuals and detection of outliers.

Supports validation of any model class with a consistent grammar.

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 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.