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)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.BenchmarkResult,
benchmark,
convertBMRToRankMatrix,
friedmanPostHocTestBMR,
friedmanTestBMR,
generateCritDifferencesData,
getBMRAggrPerformances,
getBMRFeatSelResults,
getBMRFilteredFeatures,
getBMRLearnerIds,
getBMRLearnerShortNames,
getBMRLearners,
getBMRMeasureIds,
getBMRMeasures, getBMRModels,
getBMRPerformances,
getBMRPredictions,
getBMRTaskIds,
getBMRTuneResults,
plotBMRBoxplots,
plotBMRSummary,
plotCritDifferencesOther plot: plotBMRBoxplots,
plotBMRSummary,
plotCalibration,
plotCritDifferences,
plotFilterValuesGGVIS,
plotFilterValues,
plotLearningCurveGGVIS,
plotLearningCurve,
plotPartialPredictionGGVIS,
plotPartialPrediction,
plotROCCurves,
plotThreshVsPerfGGVIS,
plotThreshVsPerf