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.
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classificationdata-sciencemachine-learningmlr3regression
15.45 score 1.1k stars 51 dependents 3.9k scripts 15k downloadsmlr3learners - 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.
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classificationlearnersmachine-learningmlr3regression
12.72 score 98 stars 20 dependents 2.5k scripts 17k 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.
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bbotkhyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimizationtunetuning
12.70 score 60 stars 22 dependents 868 scripts 12k downloads
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'.
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machine-learningmiscellaneousmlr3
11.12 score 13 stars 59 dependents 502 scripts 14k downloadsbbotk - 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'.
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bbotkblack-box-optimizationdata-sciencehyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3optimization
11.08 score 26 stars 26 dependents 284 scripts 15k downloadsmlr3extralearners - Extra Learners For mlr3
Extra learners for use in mlr3.
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machine-learningmlr3
10.07 score 115 stars 862 scripts
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.
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ggplot2mlr3visualizationvisualizations
9.92 score 46 stars 3 dependents 574 scripts 4.5k downloadsmlr3mbo - Flexible Bayesian Optimization
A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.
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automlbayesian-optimizationbbotkblack-box-optimizationgaussian-processhpohyperparameterhyperparameter-optimizationhyperparameter-tuningmachine-learningmlr3model-based-optimizationoptimizationoptimizerrandom-foresttuning
9.58 score 27 stars 8 dependents 225 scripts 4.6k downloadsmlr3verse - 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/>.
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machine-learningmetamlr3
9.34 score 64 stars 1 dependents 1.0k scripts 4.1k 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.
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evolutionary-algorithmsexhaustive-searchfeature-selectionmachine-learningmlr3optimizationrandom-searchrecursive-feature-eliminationsequential-feature-selection
9.27 score 43 stars 3 dependents 162 scripts 3.9k downloads
mlr3filters - Filter Based Feature Selection for 'mlr3'
Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.
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feature-selectionfilterfiltersmlrmlr3variable-importance
8.33 score 21 stars 2 dependents 171 scripts 3.5k 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.
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automlbbotkhyperbandhyperparameter-tuningmachine-learningmlr3optimizationtunetuning
8.20 score 19 stars 3 dependents 67 scripts 3.1k 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'.
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mlr3raster-predictionspatialspatial-modelling
7.87 score 45 stars 51 scripts 2.0k downloadsrush - Rapid Asynchronous and Distributed Computing
Package to tackle large-scale problems asynchronously across a distributed network. Employing a database centric model, rush enables workers to communicate tasks and their results over a shared 'Redis' database. Key features include low task overhead, efficient caching, and robust error handling. The package powers the asynchronous optimization algorithms in the 'bbotk' and 'mlr3tuning' packages.
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mlr3parallel-computing
6.31 score 14 stars 1 dependents 22 scripts 1.9k downloadsmlr3db - Data Base Backend for 'mlr3'
Extends the 'mlr3' package with a backend to transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or 'PostgreSQL'. The package provides three additional backends: 'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases and also on Apache Parquet files. 'DataBackendPolars' operates on 'Polars' data frames.
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bigquerydata-backenddatabaseduckdbmachine-learningmariadbmlr3mysqlodbcpostgresqlsparksqlite
5.96 score 23 stars 12 scripts 1.9k 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.
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batchtoolscluster-computinghigh-performance-computinghpcmlr3
5.38 score 6 stars 83 scripts 1.9k downloadsmlr3data - 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.
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datadata-sciencedata-setsmachine-learningmlr3
5.30 score 2 stars 2 dependents 32 scripts 3.5k downloads