mlr3summary: Model and Learner Summaries for 'mlr3'

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: NA
Author: Susanne Dandl ORCID iD [aut, cre], Marc Becker ORCID iD [aut], Bernd Bischl ORCID iD [aut], Giuseppe Casalicchio ORCID iD [aut], Ludwig Bothmann ORCID iD [aut]
Maintainer: Susanne Dandl <dandls.datascience at gmail.com>
License: LGPL-3
NeedsCompilation: no
Language: en-US
Materials: README
CRAN checks: mlr3summary results

Documentation:

Reference manual: mlr3summary.pdf

Downloads:

Package source: mlr3summary_0.1.0.tar.gz
Windows binaries: r-devel: mlr3summary_0.1.0.zip, r-release: mlr3summary_0.1.0.zip, r-oldrel: mlr3summary_0.1.0.zip
macOS binaries: r-release (arm64): mlr3summary_0.1.0.tgz, r-oldrel (arm64): mlr3summary_0.1.0.tgz, r-release (x86_64): mlr3summary_0.1.0.tgz, r-oldrel (x86_64): mlr3summary_0.1.0.tgz

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