View source: R/LearnerClassifGlmnet.R
autoplot.LearnerClassifCVGlmnet | R Documentation |
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.
## 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(),
...
)
object |
(mlr3learners::LearnerClassifGlmnet | mlr3learners::LearnerRegrGlmnet | mlr3learners::LearnerRegrCVGlmnet | mlr3learners::LearnerRegrCVGlmnet). |
type |
(character(1)): |
task |
(mlr3::Task) |
grid_points |
(integer(1)) |
expand_range |
(numeric(1)) |
theme |
( |
... |
(ignored). |
ggplot2::ggplot()
.
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")}.
## 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)
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