
Plots for GLMNet Learners
Source:R/LearnerClassifCVGlmnet.R, R/LearnerClassifGlmnet.R, R/LearnerRegrCVGlmnet.R, and 1 more
      autoplot.LearnerClassifGlmnet.RdVisualizations 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). 
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. doi:10.32614/RJ-2016-060 .
Examples
if (FALSE) { # \dontrun{
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")
} # }