autoplot.BenchmarkResult: Plot for BenchmarkResult

View source: R/BenchmarkResult.R

autoplot.BenchmarkResultR Documentation

Plot for BenchmarkResult

Description

Generates plots for mlr3::BenchmarkResult, depending on argument type:

  • "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. Additional arguments will be passed down to the respective autoplot() function in package precrec. Arguments calc_avg and cb_alpha are passed to precrec::evalmod().

  • "prc": Precision recall curve. See "roc".

Usage

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

Arguments

object

(mlr3::BenchmarkResult).

type

(character(1)):
Type of the plot. See description.

measure

(mlr3::Measure)
Performance measure to use.

...

(any): Additional arguments, passed down to the respective geom or plotting function.

Value

ggplot2::ggplot() object.

Theme

The theme_mlr3() and viridis color maps are applied by default to all autoplot() methods. To change this behavior set options(mlr3.theme = FALSE).

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")
}

mlr3viz documentation built on Aug. 15, 2022, 5:07 p.m.