mlr (version 2.19.1)

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
)

Value

ggplot2 plot object.

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.

See Also

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