Confidence interval and resampling methods for inference on
    the generalization error.

Recommended Learners for 'mlr3'. Extends 'mlr3' with
    interfaces to essential machine learning packages on CRAN.  This
    includes, but is not limited to: (penalized) linear and logistic
    regression, linear and quadratic discriminant analysis, k-nearest
    neighbors, naive Bayes, support vector machines, and gradient
    boosting.

A modern and flexible approach to Bayesian Optimization / Model
    Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox
    providing both ready-to-use optimization algorithms as well as their fundamental
    building blocks allowing for straightforward implementation of custom
    algorithms. Single- and multi-objective optimization is supported as well as
    mixed continuous, categorical and conditional search spaces. Moreover, using
    'mlr3mbo' for hyperparameter optimization of machine learning models within the
    'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use
    optimization algorithms include Efficient Global Optimization by Jones et al.
    (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006)
    <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008)
    <doi:10.1007/978-3-540-87700-4_78>.

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.