mlr: plotBMRRanksAsBarChart – R documentation

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plotBMRRanksAsBarChart

Create a bar chart for ranks in a BenchmarkResult.

Description

Plots a bar chart from the ranks of algorithms. Alternatively, tiles can be plotted for every rank-task combination, see pos for details. In all plot variants the ranks of the learning algorithms are displayed on the x-axis. Areas are always colored according to the learner.id.

Usage

plotBMRRanksAsBarChart(
  bmr,
  measure = NULL,
  ties.method = "average",
  aggregation = "default",
  pos = "stack",
  order.lrns = NULL,
  order.tsks = NULL,
  pretty.names = TRUE
)

Arguments

`bmr`	(BenchmarkResult) Benchmark result.
`measure`	(Measure) Performance measure. Default is the first measure used in the benchmark experiment.
`ties.method`	(`character(1)`) See rank for details.
`aggregation`	(`character(1)`) “mean” or “default”. See getBMRAggrPerformances for details on “default”.
`pos`	(`character(1)`) Optionally set how the bars are positioned in ggplot2. Ranks are plotted on the x-axis. “tile” plots a heat map with `task` as the y-axis. Allows identification of the performance in a special task. “stack” plots a stacked bar plot. Allows for comparison of learners within and and across ranks. “dodge” plots a bar plot with bars next to each other instead of stacked bars.
`order.lrns`	(`character(n.learners)`) Character vector with `learner.ids` in new order.
`order.tsks`	(`character(n.tasks)`) Character vector with `task.ids` in new order.
`pretty.names`	(`logical(1)`) Whether to use the short name of the learner instead of its ID in labels. Defaults to `TRUE`.

Value

ggplot2 plot object.

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

Examples

# see benchmark

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