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.

A supervised learning algorithm inputs a train set,
 and outputs a prediction function, which can be used on a test set.
 If each data point belongs to a subset
 (such as geographic region, year, etc), then
 how do we know if subsets are similar enough so that
 we can get accurate predictions on one subset, 
 after training on Other subsets?
 And how do we know if training on All subsets would improve
 prediction accuracy, relative to training on the Same subset?
 SOAK, Same/Other/All K-fold cross-validation, <doi:10.48550/arXiv.2410.08643>
 can be used to answer these questions, by fixing a test subset,
 training models on Same/Other/All subsets, and then
 comparing test error rates (Same versus Other and Same versus All).
 Also provides code for estimating how many train samples
 are required to get accurate predictions on a test set.

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 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>.

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.