friedmanTestBMR

[<code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.9" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code>]
Benchmark result.

[<code><a rd-options="" href="/link/Measure?package=mlr&version=2.9" data-mini-rdoc="mlr::Measure">Measure</a></code>]
Performance measure.
Default is the first measure used in the benchmark experiment.

measure

[<code>character(1)</code>] 
&#147;mean&#148; or &#147;default&#148;. See <code><a rd-options="" href="/link/getBMRAggrPerformances?package=mlr&version=2.9" data-mini-rdoc="mlr::getBMRAggrPerformances">getBMRAggrPerformances</a></code>
for details on &#147;default&#148;.

aggregation


Performs a <code><a rd-options="stats" href="/link/friedman.test?package=mlr&version=2.9&to=stats" data-mini-rdoc="stats::friedman.test">friedman.test</a></code> for a selected measure.
The null hypothesis is that apart from an effect of the different
[<code><a rd-options="" href="/link/Task?package=mlr&version=2.9" data-mini-rdoc="mlr::Task">Task</a></code>], the location parameter (aggregated performance-measure)
is the same for each <code><a rd-options="" href="/link/Learner?package=mlr&version=2.9" data-mini-rdoc="mlr::Learner">Learner</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.

Bernd Bischl

Machine Learning in R

friedmanTestBMR function

[<code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code>]
Benchmark result.

[<code><a rd-options='' href='Measure'>Measure</a></code>]
Performance measure.
Default is the first measure used in the benchmark experiment.

[<code>character(1)</code>] 
&#147;mean&#148; or &#147;default&#148;. See <code><a rd-options='' href='getBMRAggrPerformances'>getBMRAggrPerformances</a></code>
for details on &#147;default&#148;.


Performs a <code><a rd-options='stats' href='friedman.test'>friedman.test</a></code> for a selected measure.
The null hypothesis is that apart from an effect of the different
[<code><a rd-options='' href='Task'>Task</a></code>], the location parameter (aggregated performance-measure)
is the same for each <code><a rd-options='' href='Learner'>Learner</a></code>.


friedmanTestBMR: Perform overall Friedman test for a BenchmarkResult.

Description

Usage

Arguments

Value

See Also

Examples