Hyperparameter optimization package of the 'mlr3' ecosystem.
It features highly configurable search spaces via the 'paradox'
package and finds optimal hyperparameter configurations for any 'mlr3'
learner. 'mlr3tuning' works with several optimization algorithms e.g.
Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo')
and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically
optimize learners and estimate the performance of optimized models
with nested resampling.
| Version: |
1.2.1 |
| Depends: |
mlr3 (≥ 0.20.0), paradox (≥ 1.0.1), R (≥ 3.1.0) |
| Imports: |
bbotk (≥ 1.4.0), checkmate (≥ 2.0.0), data.table, lgr, mlr3misc (≥ 0.15.1), R6 |
| Suggests: |
adagio, future, GenSA, irace, knitr, mlflow, mlr3learners (≥
0.7.0), mlr3pipelines (≥ 0.5.2), nloptr, rush, rmarkdown, rpart, testthat (≥ 3.0.0), xgboost |
| Published: |
2024-11-26 |
| DOI: |
10.32614/CRAN.package.mlr3tuning |
| Author: |
Marc Becker  [cre,
aut],
Michel Lang [aut],
Jakob Richter
[aut],
Bernd Bischl
[aut],
Daniel Schalk
[aut] |
| Maintainer: |
Marc Becker <marcbecker at posteo.de> |
| BugReports: |
https://github.com/mlr-org/mlr3tuning/issues |
| License: |
LGPL-3 |
| URL: |
https://mlr3tuning.mlr-org.com,
https://github.com/mlr-org/mlr3tuning |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
mlr3tuning results |