Plot for Classification Learners

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

• "prediction" (default): Decision boundary of the learner and the true class labels.

Usage

# S3 method for class 'LearnerClassif'
autoplot(
  object,
  type = "prediction",
  task,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)

Arguments

object

(mlr3::LearnerClassif).

type

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

task

(mlr3::Task)
Train task.

grid_points

(integer(1))
Number of grid points per feature dimension.

expand_range

(numeric(1))
Expand the range of the grid.

theme

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

...

(ignored).

Value

ggplot2::ggplot().

Examples

# \donttest{
if (requireNamespace("mlr3")) {
library(mlr3)
library(mlr3viz)

task = tsk("pima")$select(c("age", "pedigree"))
learner = lrn("classif.rpart", predict_type = "prob")
learner$train(task)

autoplot(learner, type = "prediction", task)
}

# }