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.
| Version: |
0.1.2 |
| 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) |
| Published: |
2026-02-18 |
| DOI: |
10.32614/CRAN.package.mlr3summary |
| Author: |
Susanne Dandl 
[aut, cre],
Marc Becker [aut],
Bernd Bischl
[aut],
Giuseppe Casalicchio
[aut],
Ludwig Bothmann
[aut] |
| Maintainer: |
Susanne Dandl <dandls.datascience at gmail.com> |
| BugReports: |
https://github.com/mlr-org/mlr3summary/issues |
| License: |
LGPL-3 |
| URL: |
https://github.com/mlr-org/mlr3summary |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
mlr3summary citation info |
| Materials: |
NEWS |
| CRAN checks: |
mlr3summary results |