mlr_learners_classif.cv_glmnet: GLM with Elastic Net Regularization Classification Learner

In mlr3learners: Recommended Learners for 'mlr3'

GLM with Elastic Net Regularization Classification Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

The default for hyperparameter family is set to "binomial" or "multinomial", depending on the number of classes.

Custom mlr3 parameters

• seed:

  • Optional integer used to seed the call to glmnet::cv.glmnet(), making its random fold assignment, and therefore the selected lambda, reproducible.

  • The global random state is reset afterwards, so it is left unchanged.

  • Defaults to NA, in which case no seed is set and the global random state is used.

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.cv_glmnet")
lrn("classif.cv_glmnet")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Id	Type	Default	Levels	Range
lambda	untyped	NULL		-
type.measure	character	deviance	deviance, class, auc, mse, mae	-
nfolds	integer	10		[3, \infty)
foldid	untyped	NULL		-
alignment	character	lambda	lambda, fraction	-
grouped	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
parallel	logical	FALSE	TRUE, FALSE	-
gamma	untyped	c(0, 0.25, 0.5, 0.75, 1)		-
relax	logical	FALSE	TRUE, FALSE	-
trace.it	integer	0		[0, 1]
alpha	numeric	1		[0, 1]
nlambda	integer	100		[1, \infty)
lambda.min.ratio	numeric	-		[0, 1]
standardize	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
exclude	untyped	NULL		-
penalty.factor	untyped	-		-
lower.limits	untyped	-Inf		-
upper.limits	untyped	Inf		-
type.logistic	character	-	Newton, modified.Newton	-
type.multinomial	character	-	ungrouped, grouped	-
maxp	integer	-		[1, \infty)
path	logical	FALSE	TRUE, FALSE	-
fdev	numeric	1e-05		[0, 1]
devmax	numeric	0.999		[0, 1]
eps	numeric	1e-06		[0, 1]
big	numeric	9.9e+35		(-\infty, \infty)
mnlam	integer	5		(-\infty, \infty)
pmin	numeric	1e-09		[0, 1]
exmx	numeric	250		(-\infty, \infty)
prec	numeric	1e-10		(-\infty, \infty)
mxit	integer	100		[1, \infty)
epsnr	numeric	1e-06		[0, 1]
mxitnr	integer	25		[1, \infty)
thresh	numeric	1e-07		[0, \infty)
maxit	integer	100000		[1, \infty)
dfmax	integer	NULL		(-\infty, \infty)
pmax	integer	NULL		(-\infty, \infty)
s	numeric	lambda.1se		[0, \infty)
predict.gamma	numeric	gamma.1se		[0, 1]
exact	logical	FALSE	TRUE, FALSE	-
use_pred_offset	logical	-	TRUE, FALSE	-
seed	integer	-		(-\infty, \infty)

Internal Encoding

Starting with mlr3 v0.5.0, the order of class labels is reversed prior to model fitting to comply to the stats::glm() convention that the negative class is provided as the first factor level.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCVGlmnet

Methods

Public methods

• LearnerClassifCVGlmnet$new()

• LearnerClassifCVGlmnet$selected_features()

• LearnerClassifCVGlmnet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

LearnerClassifCVGlmnet$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifCVGlmnet$new()

---

LearnerClassifCVGlmnet$selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerClassifCVGlmnet$selected_features(lambda = NULL)

Arguments

lambda

(numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.

---

LearnerClassifCVGlmnet$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v033.i01")}.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

# Define the Learner and set parameter values
learner = lrn("classif.cv_glmnet")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# Print the model
print(learner$model)

# Importance method
if ("importance" %in% learner$properties) print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr3learners documentation built on July 25, 2026, 5:06 p.m.