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 2.16.0 Index]