Container for results of benchmarked experiments using <code><a href="/link/benchmark?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::benchmark">benchmark</a></code>.
The structure of the object itself is rather complicated, it is recommended to
retrive required information via <code>link{getAggrPerformances}</code>, <code>link{getPredictions}</code>,
<code><a href="/link/getPerformances?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getPerformances">getPerformances</a></code>, <code><a href="/link/getFeatSelResult?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getFeatSelResult">getFeatSelResult</a></code>, <code><a href="/link/getTuneResult?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getTuneResult">getTuneResult</a></code> or
<code><a href="/link/getFilterResult?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getFilterResult">getFilterResult</a></code>. Alternatively, you can convert the object using
<code><a href="/link/as.data.frame?package=mlr&version=2.0&to=base" rd-options="base" data-mini-rdoc="base::as.data.frame">as.data.frame</a></code>

BenchmarkResult

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.

BenchmarkResult: Result of a benchmark run.

Description

Arguments

See Also