mlr3viz (version 0.11.0)

autoplot.LearnerClassifCVGlmnet: Plots for GLMNet Learners

Description

Visualizations for mlr3learners::LearnerClassifGlmnet. 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.

• "ggfortify": Visualizes the model using the package ggfortify.

Usage

# S3 method for LearnerClassifCVGlmnet
autoplot(
  object,
  type = "prediction",
  task = NULL,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)
# S3 method for LearnerClassifGlmnet
autoplot(
  object,
  type = "prediction",
  task = NULL,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)
# S3 method for LearnerRegrCVGlmnet
autoplot(
  object,
  type = "prediction",
  task = NULL,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)
# S3 method for LearnerRegrGlmnet
autoplot(
  object,
  type = "prediction",
  task = NULL,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)

Value

ggplot2::ggplot().

Arguments

object

(mlr3learners::LearnerClassifGlmnet | mlr3learners::LearnerRegrGlmnet | mlr3learners::LearnerClassifCVGlmnet | mlr3learners::LearnerRegrCVGlmnet).

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).

References

Tang Y, Horikoshi M, Li W (2016). “ggfortify: Unified Interface to Visualize Statistical Result of Popular R Packages.” The R Journal, 8(2), 474--485. tools:::Rd_expr_doi("10.32614/RJ-2016-060").

Examples

if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
if (FALSE) {
library(mlr3learners)

# classification
task = tsk("sonar")
learner = lrn("classif.glmnet")
learner$train(task)
autoplot(learner, type = "ggfortify")

# regression
task = tsk("mtcars")
learner = lrn("regr.glmnet")
learner$train(task)
autoplot(learner, type = "ggfortify")
}
}