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.
Furhtermore, your learners can be automatically tuned using <code><a href="/link/makeTuneWrapper?package=mlr&version=2.0" 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 a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

benchmark: Benchmark experiment for multiple learners and tasks.

Description

Usage

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Value

See Also