getBMRModels

(<a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.13" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a>)
Benchmark result.

(<code>character(1)</code>)
Restrict result to certain tasks.
Default is all.

task.ids

(<code>character(1)</code>)
Restrict result to certain learners.
Default is all.

learner.ids

(<code>logical(1)</code>)
If drop is <code>FALSE</code> (the default), a nested list with
the following structure is returned:
<code>res[task.ids][learner.ids]</code>.
If drop is set to <code>TRUE</code> it is checked if the list
structure can be simplified.
If only one learner was passed, a list with entries
for each task is returned.
If only one task was passed, the entries are named after
the corresponding learner.
For an experiment with both one task and learner,
the whole list structure is removed.
Note that the name of the
task/learner will be dropped from the return object.

drop

A list of lists containing all <a rd-options="" href="/link/WrappedModel?package=mlr&version=2.13" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a>s trained in the benchmark experiment.
If <code>models</code> is <code>FALSE</code> in the call to <a rd-options="" href="/link/benchmark?package=mlr&version=2.13" data-mini-rdoc="mlr::benchmark">benchmark</a>, the function will return <code>NULL</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

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Patrick Schratz

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

getBMRModels function

(<a rd-options='' href='BenchmarkResult'>BenchmarkResult</a>)
Benchmark result.

A list of lists containing all <a rd-options='' href='WrappedModel'>WrappedModel</a>s trained in the benchmark experiment.
If <code>models</code> is <code>FALSE</code> in the call to <a rd-options='' href='benchmark'>benchmark</a>, the function will return <code>NULL</code>.

Extract all models from benchmark result. — getBMRModels

Extract all models from benchmark result.

getBMRModels: Extract all models from benchmark result.

Description

Usage

Arguments

Value

See Also