plotBMRRanksAsBarChart: Create a bar chart for ranks in a BenchmarkResult.

In mlr: Machine Learning in R

Create a bar chart for ranks in a BenchmarkResult.

Description

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.

Usage

plotBMRRanksAsBarChart(
  bmr,
  measure = NULL,
  ties.method = "average",
  aggregation = "default",
  pos = "stack",
  order.lrns = NULL,
  order.tsks = NULL,
  pretty.names = TRUE
)

Arguments

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

Value

ggplot2 plot object.

See Also

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()

Examples

# see benchmark

mlr documentation built on June 22, 2024, 10:51 a.m.