measure across all iterations
of the resampling strategy, faceted by the task.id.plotBMRBoxplots(bmr, measure = NULL, style = "box", order.lrns = NULL,
order.tsks = NULL, pretty.names = TRUE, facet.wrap.nrow = NULL,
facet.wrap.ncol = NULL)BenchmarkResult]
Benchmark result.Measure]
Performance measure.
Default is the first measure used in the benchmark experiment.character(1)]
Type of plot, can be “box” for a boxplot or “violin” for a violin plot.
Default is “box”.character(n.learners)]
Character vector with learner.ids in new order.character(n.tasks)]
Character vector with task.ids in new order.integer()]
Number of rows and columns for facetting. Default for both is NULL.
In this case ggplot's facet_wrap will choose the layout itself.plotBMRRanksAsBarChart,
plotBMRSummary,
plotCalibration,
plotCritDifferences,
plotFilterValuesGGVIS,
plotLearningCurveGGVIS,
plotLearningCurve,
plotPartialDependenceGGVIS,
plotPartialDependence,
plotROCCurves, plotResiduals,
plotThreshVsPerfGGVIS,
plotThreshVsPerf Other benchmark: BenchmarkResult,
benchmark,
convertBMRToRankMatrix,
friedmanPostHocTestBMR,
friedmanTestBMR,
generateCritDifferencesData,
getBMRAggrPerformances,
getBMRFeatSelResults,
getBMRFilteredFeatures,
getBMRLearnerIds,
getBMRLearnerShortNames,
getBMRLearners,
getBMRMeasureIds,
getBMRMeasures, getBMRModels,
getBMRPerformances,
getBMRPredictions,
getBMRTaskDescriptions,
getBMRTaskIds,
getBMRTuneResults,
plotBMRRanksAsBarChart,
plotBMRSummary,
plotCritDifferences