Mlr3summary: Concise and Interpretable Summaries for Machine Learning Models

MCML Authors

Susanne Dandl

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Marc Becker

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Ludwig Bothmann

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models; Second, the summary output is more extensive and customizable -- it comprises information on the dataset, model performance, model complexity, model's estimated feature importances, feature effects, and fairness metrics; Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc. Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike.

inproceedings DBB+24a

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xAI 2024

Demo Track of the 2nd World Conference on Explainable Artificial Intelligence. Valletta, Malta, Jul 17-19, 2024.

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Authors

S. Dandl • M. Becker • B. Bischl • G. Casalicchio • L. Bothmann

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Links

arXiv

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Research Area

 A1 | Statistical Foundations & Explainability