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.

The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
  incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
  interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
  learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
  of general objective functions using the 'bbotk' black box optimization framework. The
  control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.

Flexible and comprehensive R toolbox for model-based
    optimization ('MBO'), also known as Bayesian optimization. It
    implements the Efficient Global Optimization Algorithm and is designed
    for both single- and multi- objective optimization with mixed
    continuous, categorical and conditional parameters. The machine
    learning toolbox 'mlr' provide dozens of regression learners to model
    the performance of the target algorithm with respect to the parameter
    settings. It provides many different infill criteria to guide the
    search process. Additional features include multi-point batch
    proposal, parallel execution as well as visualization and
    sophisticated logging mechanisms, which is especially useful for
    teaching and understanding of algorithm behavior. 'mlrMBO' is
    implemented in a modular fashion, such that single components can be
    easily replaced or adapted by the user for specific use cases.