getBMRFeatSelResults

[<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

[<code>character(1)</code>]
Return one data.frame as result - or a list of lists of objects?.
Default is <code>FALSE</code>.

as.df


Returns a nested list of <code><a rd-options="" href="/link/FeatSelResult?package=mlr&version=2.9" data-mini-rdoc="mlr::FeatSelResult">FeatSelResult</a></code>s. The first level of nesting is by data set, the second by learner, the third for the benchmark resampling iterations. If <code>as.df</code> is <code>TRUE</code>, a data frame with &#147;task.id&#148;, &#147;learner.id&#148;, the resample iteration and the selected features is returned.<p></p><p>Note that if more than one feature is selected and a data frame is requested, there will be multiple rows for the same dataset-learner-iteration; one for each selected feature.
</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

getBMRFeatSelResults function

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


Returns a nested list of <code><a rd-options='' href='FeatSelResult'>FeatSelResult</a></code>s. The first level of nesting is by data set, the second by learner, the third for the benchmark resampling iterations. If <code>as.df</code> is <code>TRUE</code>, a data frame with &#147;task.id&#148;, &#147;learner.id&#148;, the resample iteration and the selected features is returned.

Note that if more than one feature is selected and a data frame is requested, there will be multiple rows for the same dataset-learner-iteration; one for each selected feature.


getBMRFeatSelResults: Extract the feature selection results from a benchmark result.

Description

Usage

Arguments

Value

See Also