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.

Last updated 1 months ago

classificationdata-sciencemachine-learningmlr3regression

14.77 score 945 stars 32 packages 2.0k scripts 10k downloads

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.

Last updated 14 days ago

bbotkhyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimizationtunetuning

11.54 score 54 stars 10 packages 368 scripts 6.7k downloads

mlr3learners - Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

Last updated 27 days ago

classificationlearnersmachine-learningmlr3regression

11.48 score 90 stars 9 packages 1.5k scripts 4.7k downloads

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'.

Last updated 14 days ago

bbotkblack-box-optimizationdata-sciencehyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimization

9.99 score 21 stars 13 packages 157 scripts 7.0k downloads

mlr3viz - Visualizations for 'mlr3'

Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.

Last updated 15 days ago

ggplot2mlr3visualizationvisualizations

9.67 score 42 stars 5 packages 372 scripts 2.8k downloads

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.

Last updated 15 days ago

evolutionary-algorithmsexhaustive-searchfeature-selectionmachine-learningmlr3optimizationrandom-searchrecursive-feature-eliminationsequential-feature-selection

8.12 score 20 stars 2 packages 60 scripts 3.8k downloads

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/>.

Last updated 5 months ago

machine-learningmetamlr3

7.94 score 51 stars 1 packages 812 scripts 2.4k downloads

mlr3hyperband - Hyperband for 'mlr3'

Successive Halving (Jamieson and Talwalkar (2016) <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018 <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

Last updated 5 months ago

automlbbotkhyperbandhyperparameter-tuningmachine-learningmlr3optimizationtunetuning

7.50 score 18 stars 3 packages 47 scripts 1.8k downloads

mlr3spatial - Support for Spatial Objects Within the 'mlr3' Ecosystem

Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'.

Last updated 9 months ago

mlr3raster-predictionspatialspatial-modelling

6.85 score 42 stars 56 scripts 448 downloads

mlr3data - Collection of Machine Learning Data Sets for 'mlr3'

A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (<https://mlr3book.mlr-org.com>), the use case gallery (<https://mlr3gallery.mlr-org.com>), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded.

Last updated 14 days ago

datadata-sciencedata-setsmachine-learningmlr3

5.21 score 2 stars 2 packages 16 scripts 2.8k downloads

mlr3batchmark - Batch Experiments for 'mlr3'

Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

Last updated 1 years ago

batchtoolscluster-computinghigh-performance-computinghpcmlr3

5.09 score 5 stars 49 scripts 224 downloads

rush - Rapid Parallel and Distributed Computing

Parallel computing with a network of local and remote workers. Fast exchange of results between the workers through a 'Redis' database. Key features include task queues, local caching, and sophisticated error handling.

Last updated 16 days ago

mlr3parallel-computing

4.91 score 9 stars 5 scripts 616 downloads