R: Plot a benchmark summary.

plotBMRSummary {mlr} | R Documentation

## Plot a benchmark summary.

### Description

Creates a scatter plot, where each line refers to a task. On that line the aggregated scores for all learners are plotted, for that task. Optionally, you can apply a rank transformation or just use one of ggplot2's transformations like ggplot2::scale_x_log10.

### Usage

    plotBMRSummary(
      bmr,
      measure = NULL,
      trafo = "none",
      order.tsks = NULL,
      pointsize = 4L,
      jitter = 0.05,
      pretty.names = TRUE
    )

### Arguments

`bmr` | (BenchmarkResult) Benchmark result.
`measure` | (Measure) Performance measure. Default is the first measure used in the benchmark experiment.
`trafo` | (`character(1)`) Currently either “none” or “rank”, the latter performing a rank transformation (with average handling of ties) of the scores per task. NB: You can add always add ggplot2::scale_x_log10 to the result to put scores on a log scale. Default is “none”.
`order.tsks` | (`character(n.tasks)`) Character vector with `task.ids` in new order.
`pointsize` | (`numeric(1)`) Point size for ggplot2 ggplot2::geom_point for data points. Default is 4.
`jitter` | (`numeric(1)`) Small vertical jitter to deal with overplotting in case of equal scores. Default is 0.05.
`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 benchmark: `BenchmarkResult`, `batchmark()`, `benchmark()`, `convertBMRToRankMatrix()`, `friedmanPostHocTestBMR()`, `friedmanTestBMR()`, `generateCritDifferencesData()`, `getBMRAggrPerformances()`, `getBMRFeatSelResults()`, `getBMRFilteredFeatures()`, `getBMRLearnerIds()`, `getBMRLearnerShortNames()`, `getBMRLearners()`, `getBMRMeasureIds()`, `getBMRMeasures()`, `getBMRModels()`, `getBMRPerformances()`, `getBMRPredictions()`, `getBMRTaskDescs()`, `getBMRTaskIds()`, `getBMRTuneResults()`, `plotBMRBoxplots()`, `plotBMRRanksAsBarChart()`, `plotCritDifferences()`, `reduceBatchmarkResults()`

Other plot: `createSpatialResamplingPlots()`, `plotBMRBoxplots()`, `plotBMRRanksAsBarChart()`, `plotCalibration()`, `plotCritDifferences()`, `plotLearningCurve()`, `plotPartialDependence()`, `plotROCCurves()`, `plotResiduals()`, `plotThreshVsPerf()`

### Examples

    # see benchmark

[Package mlr version 2.17.1 Index]