Plots a bar chart from the ranks of algorithms. Alternatively,
tiles can be plotted for every rank-task combination, see 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
.
ggplot2 plot object.
Other plot:
createSpatialResamplingPlots()
,
plotBMRBoxplots()
,
plotBMRSummary()
,
plotCalibration()
,
plotCritDifferences()
,
plotLearningCurve()
,
plotPartialDependence()
,
plotROCCurves()
,
plotResiduals()
,
plotThreshVsPerf()
Other benchmark:
BenchmarkResult
,
batchmark()
,
benchmark()
,
convertBMRToRankMatrix()
,
friedmanPostHocTestBMR()
,
friedmanTestBMR()
,
generateCritDifferencesData()
,
getBMRAggrPerformances()
,
getBMRFeatSelResults()
,
getBMRFilteredFeatures()
,
getBMRLearnerIds()
,
getBMRLearnerShortNames()
,
getBMRLearners()
,
getBMRMeasureIds()
,
getBMRMeasures()
,
getBMRModels()
,
getBMRPerformances()
,
getBMRPredictions()
,
getBMRTaskDescs()
,
getBMRTaskIds()
,
getBMRTuneResults()
,
plotBMRBoxplots()
,
plotBMRSummary()
,
plotCritDifferences()
,
reduceBatchmarkResults()
# NOT RUN {
# see benchmark
# }
Run the code above in your browser using DataLab