mlr3summary: Concise and interpretable summaries for machine learning models
This work addresses the need for better model interpretability and selection tools in machine learning, though it is incremental as it builds on existing summary functions in R.
The authors tackled the problem of generating concise and interpretable summaries for machine learning models by introducing an R package that provides model-agnostic summaries, including performance metrics, feature importances, and fairness assessments, resulting in a tool that enhances model selection efficiency for practitioners and researchers.
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.