mlr3summary: Concise and interpretable summaries for machine learning models

TL;DR

This paper introduces mlr3summary, an R package that provides comprehensive, customizable, and model-agnostic summaries of machine learning models, including performance, feature importance, effects, and fairness metrics, to aid model selection.

Contribution

The package extends the linear model summary function to non-parametric models, offering a unified, detailed, and resampling-based summary tool for diverse machine learning models.

Findings

Provides extensive model summaries including performance and fairness metrics

Supports model-agnostic and customizable output

Uses resampling for unbiased performance estimates

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.