Showing 6 of total 6 results (show query)
mlr-org
mlr3:Machine Learning in R - Next Generation
Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Maintained by Marc Becker. Last updated 18 days ago.
classificationdata-sciencemachine-learningmlr3regression
972 stars 14.86 score 2.3k scripts 35 dependentsmlr-org
mlr3tuning:Hyperparameter Optimization for 'mlr3'
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.
Maintained by Marc Becker. Last updated 3 months ago.
bbotkhyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimizationtunetuning
55 stars 11.53 score 384 scripts 11 dependentsmlr-org
mlr3misc:Helper Functions for 'mlr3'
Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Maintained by Marc Becker. Last updated 4 months ago.
machine-learningmiscellaneousmlr3
12 stars 10.28 score 302 scripts 42 dependentsmlr-org
bbotk:Black-Box Optimization Toolkit
Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'.
Maintained by Marc Becker. Last updated 4 months ago.
bbotkblack-box-optimizationdata-sciencehyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimization
22 stars 9.83 score 166 scripts 14 dependentsmlr-org
mlr3verse:Easily Install and Load the 'mlr3' Package Family
The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.
Maintained by Marc Becker. Last updated 3 months ago.
55 stars 8.32 score 720 scripts 1 dependentsmlr-org
mlr3fselect:Feature Selection for 'mlr3'
Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.
Maintained by Marc Becker. Last updated 2 months ago.
evolutionary-algorithmsexhaustive-searchfeature-selectionmachine-learningmlr3optimizationrandom-searchrecursive-feature-eliminationsequential-feature-selection
23 stars 8.12 score 70 scripts 2 dependents