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 group
 (such as geographic region, year, etc), then
 how do we know if it is possible to train on one group, and predict
 accurately on another group? Cross-validation can be used to determine
 the extent to which this is possible, by first assigning fold IDs from
 1 to K to all data (possibly using stratification, usually by group
 and label). Then we loop over test sets (group/fold combinations),
 train sets (same group, other groups, all groups), and compute
 test/prediction accuracy for each combination.  Comparing
 test/prediction accuracy between same and other, we can determine the
 extent to which it is possible (perfect if same/other have similar
 test accuracy for each group; other is usually somewhat less accurate
 than same; other can be just as bad as featureless baseline when the
 groups have different patterns).
 For more information, 
 <https://tdhock.github.io/blog/2023/R-gen-new-subsets/>
 describes the method in depth.
 How many train samples are required to get accurate predictions on a
 test set? Cross-validation can be used to answer this question, with
 variable size train sets.