A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.

Extends the 'mlr3' ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.

Extends the mlr3 ML 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.

Concise and interpretable summaries for machine learning models 
    and learners of the 'mlr3' ecosystem.
    The package takes inspiration from the summary function for (generalized)
    linear models but extends it to non-parametric machine learning models, 
    based on generalization performance, model complexity, feature importances 
    and effects, and fairness metrics.

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/>.