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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

15.72 score 1.1k stars 64 dependents 3.5k scripts 23k downloads

mlr3pipelines - Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

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baggingdata-sciencedataflow-programmingensemble-learningmachine-learningmlr3pipelinespreprocessingstacking

13.28 score 149 stars 16 dependents 850 scripts 14k 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

13.12 score 60 stars 38 dependents 880 scripts 17k 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.

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classificationlearnersmachine-learningmlr3regression

12.72 score 98 stars 20 dependents 2.5k scripts 17k downloads

paradox - Define and Work with Parameter Spaces for Complex Algorithms

Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.

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experimental-designhyperparametersmlr3transformations

11.62 score 28 stars 66 dependents 724 scripts 15k 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'.

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

11.39 score 26 stars 42 dependents 258 scripts 18k 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

11.35 score 13 stars 72 dependents 512 scripts 19k downloads

mlr3extralearners - Extra Learners For mlr3

Extra learners for use in mlr3.

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machine-learningmlr3

10.07 score 115 stars 858 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

9.85 score 46 stars 3 dependents 578 scripts 3.5k downloads

mlr3mbo - 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

9.51 score 27 stars 8 dependents 198 scripts 3.9k 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/>.

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machine-learningmetamlr3

9.31 score 64 stars 1 dependents 1.0k scripts 3.7k 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

9.22 score 43 stars 3 dependents 162 scripts 3.5k downloads

mlr3spatiotempcv - Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

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cross-validationmlr3resamplingresampling-methodsspatialtemporal

8.89 score 56 stars 203 scripts 1.7k downloads

mlr3cluster - Cluster Extension for 'mlr3'

Extends the 'mlr3' package with cluster analysis.

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cluster-analysisclusteringmachine-learningmlr3

8.58 score 27 stars 2 dependents 50 scripts 3.3k downloads

mlr3torch - Deep Learning with 'mlr3'

Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'.

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data-sciencedeep-learningmachine-learningmlr3torch

8.38 score 56 stars 2 dependents 112 scripts 3.1k 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

8.29 score 21 stars 2 dependents 157 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

8.13 score 19 stars 3 dependents 67 scripts 2.6k downloads

mlr3proba - Probabilistic Supervised Learning for 'mlr3'

Provides extensions for probabilistic supervised learning for 'mlr3'. This currently includes survival analysis, probabilistic regression and density estimation.

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density-estimationmachine-learningmlr3supervised-learningsurvival-analysiscpp

8.12 score 150 stars 1 dependents 322 scripts 138 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

7.74 score 45 stars 49 scripts 1.4k downloads

xplainfi - Feature Importance Methods for Global Explanations

Provides a consistent interface for common feature importance methods as described in Ewald et al. (2024) <doi:10.1007/978-3-031-63797-1_22>, including permutation feature importance (PFI), conditional and relative feature importance (CFI, RFI), leave one covariate out (LOCO), and Shapley additive global importance (SAGE), as well as feature sampling mechanisms to support conditional importance methods.

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feature-importanceinterpretable-machine-learningmachine-learningmlr3statistical-inferencevariable-importance

6.47 score 6 stars 13 scripts 234 downloads

rush - 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 downloads

mlr3fda - Extending 'mlr3' to Functional Data Analysis

Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'.

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data-analysisdata-analysis-in-rdata-sciencefunctional-datamachine-learningmlr3

6.03 score 7 stars 5 scripts 1.8k downloads

mlr3db - 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

5.88 score 23 stars 10 scripts 1.5k downloads

distr6 - The Complete R6 Probability Distributions Interface

An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.

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cpp

5.64 score 2 dependents 86 scripts 311 downloads

mlr3oml - Connector Between 'mlr3' and 'OpenML'

Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package.

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datadata-sciencedatasetsmachine-learningmlr3openmlcpp

5.64 score 9 stars 139 scripts 1.4k downloads

mlr3automl - Automated Machine Learning for 'mlr3'

Flexible automated machine learning (AutoML) system for the 'mlr3' ecosystem. Automatically selects a suitable machine learning algorithm and tunes its hyperparameters for a given task. Constructs preprocessing pipelines with multiple parallel branches using 'mlr3pipelines' and jointly optimizes them together with the learners using 'mlr3tuning'. The optimization is driven by asynchronous decentralized Bayesian optimization by Egele et al. (2023) <doi:10.1109/e-Science58273.2023.10254839>.

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5.52 score 6 stars 29 scripts 287 downloads

mlr3benchmark - Analysis and Visualisation of Benchmark Experiments

Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond.

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analysisbenchmark-analysisbenchmark-experimentsbenchmarkingmlr3

5.44 score 13 stars 53 scripts 1.6k downloads

mlr3forecast - Extending 'mlr3' to Time Series Forecasting

Extends the 'mlr3' package and ecosystem to time series forecasting. Provides forecasting tasks, learners, resampling strategies, performance measures, and 'mlr3pipelines' operators for time-series feature engineering. Machine learning regression learners can be turned into forecasters through recursive and direct multi-step strategies.

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forecastingmachine-learningmlr3regressiontime-series

5.34 score 8 stars 2 scripts 1.4k 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.

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datadata-sciencedata-setsmachine-learningmlr3

5.30 score 2 stars 2 dependents 32 scripts 3.5k 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

5.23 score 6 stars 82 scripts 1.4k downloads