Implements multiple performance measures for supervised
    learning.  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.

Frequently used helper functions and assertions used in
    'mlr3' and its companion packages. 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'.

Provides an interface to 'OpenML.org' to list and download
    machine learning data, tasks and experiments. The 'OpenML' objects can
    be automatically converted to 'mlr3' objects.  For a more
    sophisticated interface with more upload options, see
    the 'OpenML' package.

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:10.1002/sam.70055>
 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.