mlr: plotBMRSummary – R documentation

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plotBMRSummary

Plot a benchmark summary.

Description

Creates a scatter plot, where each line refers to a task. On that line the aggregated scores for all learners are plotted, for that task. Optionally, you can apply a rank transformation or just use one of ggplot2's transformations like ggplot2::scale_x_log10.

Usage

plotBMRSummary(
  bmr,
  measure = NULL,
  trafo = "none",
  order.tsks = NULL,
  pointsize = 4L,
  jitter = 0.05,
  pretty.names = TRUE
)

Arguments

`bmr`	(BenchmarkResult) Benchmark result.
`measure`	(Measure) Performance measure. Default is the first measure used in the benchmark experiment.
`trafo`	(`character(1)`) Currently either “none” or “rank”, the latter performing a rank transformation (with average handling of ties) of the scores per task. NB: You can add always add ggplot2::scale_x_log10 to the result to put scores on a log scale. Default is “none”.
`order.tsks`	(`character(n.tasks)`) Character vector with `task.ids` in new order.
`pointsize`	(`numeric(1)`) Point size for ggplot2 ggplot2::geom_point for data points. Default is 4.
`jitter`	(`numeric(1)`) Small vertical jitter to deal with overplotting in case of equal scores. Default is 0.05.
`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(), plotBMRRanksAsBarChart(), plotCritDifferences(), reduceBatchmarkResults()

Other plot: createSpatialResamplingPlots(), plotBMRBoxplots(), plotBMRRanksAsBarChart(), plotCalibration(), plotCritDifferences(), plotLearningCurve(), plotPartialDependence(), plotROCCurves(), plotResiduals(), plotThreshVsPerf()

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