Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.

Feature selection package of the 'mlr3' ecosystem. It selects
    the optimal feature set for any 'mlr3' learner. The package works with
    several optimization algorithms e.g. Random Search, Recursive Feature
    Elimination, and Genetic Search. Moreover, it can automatically
    optimize learners and estimate the performance of optimized feature
    sets with nested resampling.

Successive Halving (Jamieson and Talwalkar (2016)
    <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018
    <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3
    ecosystem. The implementation in mlr3hyperband features improved
    scheduling and parallelizes the evaluation of configurations. The
    package includes tuners for hyperparameter optimization in mlr3tuning
    and optimizers for black-box optimization in bbotk.

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>.