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 16 days ago
classificationdata-sciencemachine-learningmlr3regression
930 stars 14.69 score 20 dependencies 31 dependentsmlr3learners - 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 3 months ago
classificationlearnersmachine-learningmlr3regression
89 stars 11.41 score 21 dependencies 10 dependentsmlr3tuning - 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 25 days ago
bbotkhyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimizationtunetuning
53 stars 11.38 score 22 dependencies 10 dependentsbbotk - 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 26 days ago
bbotkblack-box-optimizationdata-sciencehyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimization
20 stars 9.83 score 8 dependencies 13 dependentsmlr3viz - 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 3 months ago
ggplot2mlr3visualizationvisualizations
42 stars 9.55 score 35 dependencies 5 dependentsmlr3verse - 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 3 months ago
machine-learningmetamlr3
50 stars 8.06 score 78 dependencies 1 dependentsmlr3fselect - 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 26 days ago
evolutionary-algorithmsexhaustive-searchfeature-selectionmachine-learningmlr3optimizationrandom-searchrecursive-feature-eliminationsequential-feature-selection
20 stars 7.76 score 25 dependencies 2 dependentsmlr3hyperband - 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 3 months ago
automlbbotkhyperbandhyperparameter-tuningmachine-learningmlr3optimizationtunetuning
18 stars 7.63 score 23 dependencies 3 dependentsmlr3spatial - 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 7 months ago
mlr3raster-predictionspatialspatial-modelling
42 stars 6.85 score 35 dependenciesmlr3batchmark - 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 11 months ago
batchtoolscluster-computinghigh-performance-computinghpcmlr3
5 stars 5.05 score 38 dependenciesrush - 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 3 months ago
mlr3parallel-computing
8 stars 4.86 score 13 dependencies