mlr3viz (version 0.10.1)

autoplot.BenchmarkResult: Plots for Benchmark Results

Description

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 BenchmarkResult
autoplot(
  object,
  type = "boxplot",
  measure = NULL,
  theme = theme_minimal(),
  ...
)

Value

ggplot2::ggplot().

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.

References

Saito T, Rehmsmeier M (2017). “Precrec: fast and accurate precision-recall and ROC curve calculations in R.” Bioinformatics, 33(1), 145-147. tools:::Rd_expr_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")
}