mlr: getBMRFilteredFeatures – R documentation

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getBMRFilteredFeatures

Extract the feature selection results from a benchmark result.

Description

Returns a nested list of characters The first level of nesting is by data set, the second by learner, the third for the benchmark resampling iterations. The list at the lowest level is the list of selected features. If as.df is TRUE, a data frame with “task.id”, “learner.id”, 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.

Usage

getBMRFilteredFeatures(
  bmr,
  task.ids = NULL,
  learner.ids = NULL,
  as.df = FALSE,
  drop = FALSE
)

Arguments

`bmr`	(BenchmarkResult) Benchmark result.
`task.ids`	(`character(1)`) Restrict result to certain tasks. Default is all.
`learner.ids`	(`character(1)`) Restrict result to certain learners. Default is all.
`as.df`	(`character(1)`) Return one data.frame as result - or a list of lists of objects?. Default is `FALSE`.
`drop`	(`logical(1)`) If drop is `FALSE` (the default), a nested list with the following structure is returned: `res[task.ids][learner.ids]`. If drop is set to `TRUE` 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.

Value

(list | data.frame). See above.

Other benchmark: BenchmarkResult, batchmark(), benchmark(), convertBMRToRankMatrix(), friedmanPostHocTestBMR(), friedmanTestBMR(), generateCritDifferencesData(), getBMRAggrPerformances(), getBMRFeatSelResults(), getBMRLearnerIds(), getBMRLearnerShortNames(), getBMRLearners(), getBMRMeasureIds(), getBMRMeasures(), getBMRModels(), getBMRPerformances(), getBMRPredictions(), getBMRTaskDescs(), getBMRTaskIds(), getBMRTuneResults(), plotBMRBoxplots(), plotBMRRanksAsBarChart(), plotBMRSummary(), plotCritDifferences(), reduceBatchmarkResults()

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release

getBMRFilteredFeatures

Description

Usage

Arguments

Value

See Also

mlr

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