Complete benchmark experiment to compare different learning algorithms across one or more tasks
w.r.t. a given resampling strategy. Experiments are paired, meaning always the same
training / test sets are used for the different learners.
Furthermore, you can of course pass <dquote>enhanced</dquote> learners via wrappers, e.g., a
learner can be automatically tuned using <code><a href="/link/makeTuneWrapper?package=mlr&version=2.6" rd-options="" data-mini-rdoc="mlr::makeTuneWrapper">makeTuneWrapper</a></code>.

benchmark

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.

benchmark: Benchmark experiment for multiple learners and tasks.

Description

Usage

Arguments

Value

See Also