An implementation of the Super Learner prediction algorithm from
  van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 
  using the 'mlr3' framework.

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'.

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.

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.

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/>.

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.

Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.