  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.
