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: 1.4.0 Depends: mlr3 (≥ 0.23.0), paradox (≥ 1.0.1), R (≥ 3.1.0) Imports: bbotk (≥ 1.6.0), checkmate (≥ 2.0.0), cli, data.table, lgr, mlr3misc (≥ 0.15.1), R6 Suggests: adagio, future, GenSA, irace (≥ 4.1.0), knitr, mirai, mlflow, mlr3learners (≥ 0.7.0), mlr3pipelines (≥ 0.5.2), nloptr, rush, rmarkdown, rpart, testthat (≥ 3.0.0), xgboost Published: 2025-06-04 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 Documentation: Downloads: Reverse dependencies: Reverse depends: MantaID, mlr3hyperband, mlr3mbo, mlr3tuningspaces Reverse imports: DoubleML, mlr3verse, mlrintermbo, sense, SIAMCAT Reverse suggests: miesmuschel, mlr3resampling, mlr3spatiotempcv, mlr3torch, mlr3viz Linking:Please use the canonical form https://CRAN.R-project.org/package=mlr3tuning to link to this page.
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