getBMRModels

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


A list of lists containing all <code><a rd-options="" href="/link/WrappedModel?package=mlr&version=2.9" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code>s trained in the benchmark experiment.<p></p><p>If <code>models</code> is <code>FALSE</code> in the call to <code><a rd-options="" href="/link/benchmark?package=mlr&version=2.9" data-mini-rdoc="mlr::benchmark">benchmark</a></code>, the function will return <code>NULL</code>.
</p>

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

getBMRModels function

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


A list of lists containing all <code><a rd-options='' href='WrappedModel'>WrappedModel</a></code>s trained in the benchmark experiment.

If <code>models</code> is <code>FALSE</code> in the call to <code><a rd-options='' href='benchmark'>benchmark</a></code>, the function will return <code>NULL</code>.


getBMRModels: Extract all models from benchmark result.

Description

Usage

Arguments

Value

See Also