plotBMRSummary: Plot a benchmark summary.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

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 scale_x_log10.

1 2	plotBMRSummary(bmr, measure = NULL, trafo = "none", order.tsks = NULL, pointsize = 4L, jitter = 0.05, pretty.names = TRUE)

`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 `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 `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`.