Plots for Benchmark Results — autoplot.BenchmarkResult • mlr3viz

Visualizations for mlr3::BenchmarkResult. The argument type controls what kind of plot is drawn. Possible choices are:

"boxplot" (default): Boxplots of performance measures, one box per mlr3::Learner and one facet per mlr3::Task.
"roc": ROC curve (1 - specificity on x, sensitivity on y). The mlr3::BenchmarkResult may only have a single mlr3::Task and a single mlr3::Resampling. Note that you can subset any mlr3::BenchmarkResult with its $filter() method (see examples). Requires package precrec.
"prc": Precision recall curve. See "roc".
"ci": Plot confidence intervals. Pass a msr("ci", ...) from the mlr3inferr package as argument measure.

Usage

# S3 method for class 'BenchmarkResult'
autoplot(
  object,
  type = "boxplot",
  measure = NULL,
  theme = theme_minimal(),
  ...
)

Arguments

object: (mlr3::BenchmarkResult).
type: (character(1)):
Type of the plot. See description.
measure: (mlr3::Measure)
Performance measure to use.
theme: (ggplot2::theme())
The ggplot2::theme_minimal() is applied by default to all plots.
...: arguments passed on to precrec::autoplot() for type = "roc" or "prc". Useful to e.g. remove confidence bands with show_cb = FALSE.

Value

ggplot2::ggplot().

References

Saito T, Rehmsmeier M (2017). “Precrec: fast and accurate precision-recall and ROC curve calculations in R.” Bioinformatics, 33(1), 145-147. doi:10.1093/bioinformatics/btw570 .

Examples

if (requireNamespace("mlr3")) {
  library(mlr3)
  library(mlr3viz)

  tasks = tsks(c("pima", "sonar"))
  learner = lrns(c("classif.featureless", "classif.rpart"),
    predict_type = "prob")
  resampling = rsmps("cv")
  object = benchmark(benchmark_grid(tasks, learner, resampling))

  head(fortify(object))
  autoplot(object)
  autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
}