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mlr3tuning
View on CRAN: Click
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Download and install mlr3tuning package within the R console
Install from CRAN:
install.packages("mlr3tuning")
Install from Github:
library("remotes")
install_github("cran/mlr3tuning")
Install by package version:
library("remotes")
install_version("mlr3tuning", "1.2.1")
Attach the package and use:
library("mlr3tuning")
Maintained by
Marc Becker
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-09-30
Latest Update: 2023-06-26
Description:
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.
How to cite:
Marc Becker (2019). mlr3tuning: Hyperparameter Optimization for 'mlr3'. R package version 1.2.1, https://cran.r-project.org/web/packages/mlr3tuning. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2019-09-30 17:20), 0.1.1 (2019-12-06 16:20), 0.1.2 (2020-01-31 22:10), 0.2.0 (2020-07-28 15:30), 0.3.0 (2020-09-08 10:10), 0.4.0 (2020-10-07 23:10), 0.5.0 (2020-12-07 22:50), 0.6.0 (2021-01-24 16:40), 0.7.0 (2021-02-11 09:10), 0.8.0 (2021-03-12 14:40), 0.9.0 (2021-09-14 09:30), 0.10.0 (2022-01-20 17:12), 0.11.0 (2022-02-02 18:00), 0.12.0 (2022-02-17 19:52), 0.12.1 (2022-02-25 14:50), 0.13.0 (2022-04-06 10:42), 0.13.1 (2022-05-03 15:20), 0.14.0 (2022-08-25 13:20), 0.15.0 (2022-10-21 12:12), 0.16.0 (2022-11-08 13:40), 0.17.0 (2022-11-19 00:20), 0.17.1 (2022-12-07 16:20), 0.17.2 (2022-12-22 08:30), 0.18.0 (2023-03-08 20:10), 0.19.0 (2023-06-26 18:50), 0.19.1 (2023-11-20 18:40), 0.19.2 (2023-11-28 12:50), 0.20.0 (2024-03-05 06:30), 1.0.0 (2024-06-29 08:40), 1.0.1 (2024-09-10 17:00), 1.0.2 (2024-10-14 21:50), 1.1.0 (2024-10-27 09:10), 1.2.0 (2024-11-08 16:10), 1.2.1 (2024-11-26 22:50)
Other packages that cited mlr3tuning R package
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Other R packages that mlr3tuning depends,
imports, suggests or enhances
Complete documentation for mlr3tuning
Functions, R codes and Examples using
the mlr3tuning R package
Some associated functions: ArchiveTuning . AutoTuner . CallbackTuning . ContextEval . ObjectiveTuning . Tuner . TunerFromOptimizer . TuningInstanceMultiCrit . TuningInstanceSingleCrit . as_search_space . auto_tuner . callback_tuning . extract_inner_tuning_archives . extract_inner_tuning_results . mlr3tuning-package . mlr3tuning.backup . mlr3tuning.early_stopping . mlr3tuning.measures . mlr_tuners . mlr_tuners_cmaes . mlr_tuners_design_points . mlr_tuners_gensa . mlr_tuners_grid_search . mlr_tuners_irace . mlr_tuners_nloptr . mlr_tuners_random_search . reexports . ti . tnr . tune . tune_nested .
Some associated R codes: ArchiveTuning.R . AutoTuner.R . CallbackTuning.R . ContextEval.R . ObjectiveTuning.R . Tuner.R . TunerCmaes.R . TunerDesignPoints.R . TunerFromOptimizer.R . TunerGenSA.R . TunerGridSearch.R . TunerIrace.R . TunerNLoptr.R . TunerRandomSearch.R . TuningInstanceMulticrit.R . TuningInstanceSingleCrit.R . as_search_space.R . assertions.R . auto_tuner.R . bibentries.R . extract_inner_tuning_archives.R . extract_inner_tuning_results.R . helper.R . mlr_callbacks.R . mlr_tuners.R . reexport.R . sugar.R . tune.R . tune_nested.R . zzz.R . Full mlr3tuning package functions and examples
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