BenchmarkResult

Result of a benchmark experiment conducted by <code><a rd-options="" href="/link/benchmark?package=mlr&version=2.8" data-mini-rdoc="mlr::benchmark">benchmark</a></code>
with the following members:
<dl>
<dt>results [list of <code><a rd-options="" href="/link/ResampleResult?package=mlr&version=2.8" data-mini-rdoc="mlr::ResampleResult">ResampleResult</a></code>]:</dt><dd>
 A nested <code>list</code> of resample results,
 first ordered by task id, then by learner id.
</dd><dt>measures [list of <code><a rd-options="" href="/link/Measure?package=mlr&version=2.8" data-mini-rdoc="mlr::Measure">Measure</a></code>]:</dt><dd>
 The performance measures used in the benchmark experiment.
</dd><dt>learners [list of <code><a rd-options="" href="/link/Learner?package=mlr&version=2.8" data-mini-rdoc="mlr::Learner">Learner</a></code>]:</dt><dd>
 The learning algorithms compared in the benchmark experiment.
</dd></dl>
The print method of this object shows aggregated performance values
for all tasks and learners.It is recommended to
retrieve required information via the <code>getBMR*</code> getter functions.
You can also convert the object using <code><a rd-options="base" href="/link/as.data.frame?package=mlr&version=2.8&to=base" data-mini-rdoc="base::as.data.frame">as.data.frame</a></code>.

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.

BenchmarkResult: BenchmarkResult object.

Description

Arguments

See Also