autoplot.EnsembleFSResult {mlr3viz}

Plots for Ensemble Feature Selection Results

Description

Visualizations for EnsembleFSResult. The argument type determines the type of plot generated. The available options are:

• "pareto" (default): Scatterplot of performance versus the number of features, possibly including the Pareto front, which allows users to decide how much performance they are willing to trade off for a more sparse model.

• "performance": Boxplot of performance across the different learners used in the ensemble feature selection process. Each box represents the distribution of scores across different resampling iterations for a particular learner.

• ⁠"n_features⁠: Boxplot of the number of features selected by each learner in the different resampling iterations.

• "stability": Barplot of stability score for each learner used in the ensemble feature selection. This plot shows how similar are the output feature sets from each learner across the different resamplings.

Usage

## S3 method for class 'EnsembleFSResult'
autoplot(
  object,
  type = "pareto",
  pareto_front = "stepwise",
  stability_measure = "jaccard",
  stability_args = NULL,
  theme = theme_minimal(),
  ...
)

Arguments

object

(mlr3fselect::EnsembleFSResult).

type

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

pareto_front

(character(1))
Type of pareto front to plot. Can be "stepwise" (default), "estimated" or "none".

stability_measure

(character(1))
The stability measure to be used in case type = "stability". One of the measures returned by stabm::listStabilityMeasures() in lower case. Default is "jaccard".

stability_args

(list)
Additional arguments passed to the stability measure function.

theme

(ggplot2::theme())
The ggplot2::theme_minimal() is applied by default to all plots.

...

(ignored).

Value

ggplot2::ggplot().

Examples

if (requireNamespace("mlr3")) {
  library(mlr3)
  library(mlr3fselect)

  set.seed (42)
  efsr = ensemble_fselect(
    fselector = fs("random_search"),
    task = tsk("sonar"),
    learners = lrns(c("classif.rpart", "classif.featureless")),
    init_resampling = rsmp("subsampling", repeats = 5),
    inner_resampling = rsmp("cv", folds = 3),
    measure = msr("classif.ce"),
    terminator = trm("evals", n_evals = 5)
  )

  # Pareto front (default, stepwise)
  autoplot(efsr)

  # Pareto front (estimated)
  autoplot(efsr, pareto_front = "estimated")

  # Performance
  autoplot(efsr, type = "performance")

  # Number of features
  autoplot(efsr, type = "n_features")

  # stability
  autoplot(efsr, type = "stability")
}