Model and Learner Summaries for 'mlr3' R package mlr3summary version 0.1.2 Concise and interpretable summaries for machine learning models and learners of the 'mlr3' ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics. Software Depends: R (>= 3.5.0) Imports: backports, checkmate (>= 2.0.0), cli, data.table, future.apply (>= 1.5.0), mlr3 (>= 0.12.0), mlr3misc Suggests: fastshap, iml, mlr3fairness, mlr3learners, mlr3pipelines, mlr3tuning, future, ranger, rpart, testthat (>= 3.1.0) Susanne Dandl <dandls.datascience@gmail.com> Comprehensive R Archive Network (CRAN) Susanne Dandl [aut, cre] (ORCID: <https://orcid.org/0000-0003-4324-4163>), Marc Becker [aut] (ORCID: <https://orcid.org/0000-0002-8115-0400>), Bernd Bischl [aut] (ORCID: <https://orcid.org/0000-0001-6002-6980>), Giuseppe Casalicchio [aut] (ORCID: <https://orcid.org/0000-0001-5324-5966>), Ludwig Bothmann [aut] (ORCID: <https://orcid.org/0000-0002-1471-6582>) LGPL-3 2026-02-18 application/tgz https://CRAN.R-project.org/package=mlr3summary doi:10.32614/CRAN.package.mlr3summary en-US