mlr3automl: Automated Machine Learning for 'mlr3'

Flexible automated machine learning (AutoML) system for the 'mlr3' ecosystem. Automatically selects a suitable machine learning algorithm and tunes its hyperparameters for a given task. Constructs preprocessing pipelines with multiple parallel branches using 'mlr3pipelines' and jointly optimizes them together with the learners using 'mlr3tuning'. The optimization is driven by asynchronous decentralized Bayesian optimization by Egele et al. (2023) <doi:10.1109/e-Science58273.2023.10254839>.

Version: 0.1.0
Depends: mlr3 (≥ 1.6.0), mlr3tuning (≥ 1.6.0), R (≥ 3.3.0), rush (≥ 1.2.1)
Imports: bbotk (≥ 1.7.1), checkmate, data.table, lhs, mlr3learners (≥ 0.14.0), mlr3mbo (≥ 1.2.0), mlr3misc (≥ 0.15.1), mlr3pipelines, paradox (≥ 1.0.1), R6, utils
Suggests: callr, e1071, fastai, glmnet, kknn, lgr, lightgbm, MASS, mirai, mlr3extralearners, mlr3torch (≥ 0.3.3), mlr3viz, ranger, redux, reticulate, rpart, testthat (≥ 3.0.0), torch, xgboost (≥ 3.2.1.1)
Published: 2026-09-03
DOI: 10.32614/CRAN.package.mlr3automl (may not be active yet)
Author: Marc Becker ORCID iD [cre, aut, cph], Damir Pulatov [aut], Baisu Zhou [aut], Lona Koers [aut]
Maintainer: Marc Becker <marcbecker at posteo.de>
BugReports: https://github.com/mlr-org/mlr3automl/issues
License: LGPL-3
URL: https://mlr3automl.mlr-org.com, https://github.com/mlr-org/mlr3automl
NeedsCompilation: no
Additional_repositories: https://mlr-org.r-universe.dev
Materials: README, NEWS
CRAN checks: mlr3automl results

Documentation:

Reference manual: mlr3automl.html , mlr3automl.pdf

Downloads:

Package source: mlr3automl_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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