mlr3 enables efficient, object-oriented programming on the building blocks of machine learning. It 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.

This package offers features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the autoplot() generic of ggplot2. The mlr3viz package draws plots with the viridis color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and precision-recall curves.

This package provides a flexible approach to Bayesian optimization / model based optimization building on the bbotk package. The 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.

mlr3misc provides frequently used helper functions and assertions used in mlr3 and its companion packages. It 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.

This package provides a small collection of interesting and educational machine learning data sets which are used as examples in the mlr3 book Applied machine learning using mlr3 in R 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.

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.

mlr3tuning implements methods for hyperparameter tuning, e.g. Grid Search, Random Search, or Simulated Annealing. Various termination criteria can be set and combined. The class AutoTuner provides a convenient way to perform nested resampling in combination with mlr3.

This package provides an mlr3 extension that provides various resampling-based confidence interval (CI) methods for estimating the generalization error. These CI methods are implemented as mlr3 measures, enabling the evaluation of individual algorithms on specific tasks as well as the comparison of different learning algorithms.

This package extends the mlr3 package with cluster analysis.

This package 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.

This package is a 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.

This package implements multiple performance measures for supervised learning. It includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.

mlr3learners extends mlr3 and mlr3proba 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.

This package implements a successive halving and hyperband 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.

mlr3pipelines 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.

This package is a collection of search spaces for hyperparameter optimization in the mlr3 ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets.