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.plotBMRRanksAsBarChart(bmr, measure = NULL, ties.method = "average",
aggregation = "default", pos = "stack", order.lrns = NULL,
order.tsks = NULL, pretty.names = TRUE)BenchmarkResult]
Benchmark result.Measure]
Performance measure.
Default is the first measure used in the benchmark experiment.character(1)]
See rank for details.character(1)]
“mean” or “default”. See getBMRAggrPerformances
for details on “default”.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.character(n.learners)]
Character vector with learner.ids in new order.character(n.tasks)]
Character vector with task.ids in new order.logical{1}]
Whether to use the short name of the learner instead of its ID in labels. Defaults to TRUE.plotBMRBoxplots,
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,
plotBMRBoxplots,
plotBMRSummary,
plotCritDifferences