autoplot.LearnerClassifGlmnet: Plots for GLMNet Learners

In mlr3viz: Visualizations for 'mlr3'

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 class 'LearnerClassifCVGlmnet'
autoplot(
  object,
  type = "prediction",
  task = NULL,
  grid_points = 100L,
  expand_range = 0,
  theme = theme_minimal(),
  ...
)

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

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

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

Arguments

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

Value

ggplot2::ggplot().

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2016-060")}.

Examples

## Not run: 
library(mlr3)
library(mlr3viz)
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")

## End(Not run)

mlr3viz documentation built on July 1, 2024, 5:06 p.m.