Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There
    is also an experimental extension for survival analysis, clustering
    and general, example-specific cost-sensitive learning. Generic
    resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single-
    and multi-objective problems.  Filter and wrapper methods for feature
    selection. Extension of basic learners with additional operations
    common in machine learning, also allowing for easy nested resampling.
    Most operations can be parallelized.

Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.

Extends the 'mlr3' package with a connector to the package
    'batchtools'. This allows to run large-scale benchmark experiments on
    scheduled high-performance computing clusters.

Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.

Extends the 'mlr3' package with cluster analysis.

A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.

Extends the 'mlr3' package with a backend to transparently
    work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or
    'PostgreSQL'. The package provides three additional backends:
    'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
    interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data
    bases and also on Apache Parquet files. 'DataBackendPolars' operates on
    'Polars' data frames.