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: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
backports, checkmate (≥ 2.0.0), data.table, mlr3 (≥ 0.12.0), mlr3misc, cli, future.apply (≥ 1.5.0) |
| Suggests: |
testthat (≥ 3.1.0), iml, mlr3pipelines, mlr3fairness, mlr3learners, fastshap, ranger, rpart |
| Published: |
2024-04-24 |
| DOI: |
10.32614/CRAN.package.mlr3summary |
| Author: |
Susanne Dandl 
[aut, cre] (<https://orcid.org/0000-0003-4324-4163>),
Marc Becker [aut]
(<https://orcid.org/0000-0002-8115-0400>),
Bernd Bischl
[aut] (<https://orcid.org/0000-0001-6002-6980>),
Giuseppe Casalicchio
[aut]
(<https://orcid.org/0000-0001-5324-5966>),
Ludwig Bothmann
[aut] (<https://orcid.org/0000-0002-1471-6582>) |
| Maintainer: |
Susanne Dandl <dandls.datascience at gmail.com> |
| License: |
LGPL-3 |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README |
| CRAN checks: |
mlr3summary results |