mlr_learners_classif.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::glmnet() from package glmnet.

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

Details

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in an efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterparts in mlr_learners_classif.cv_glmnet() or mlr_learners_regr.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

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.glmnet")
lrn("classif.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
alpha	numeric	1		[0, 1]
nlambda	integer	100		[1, \infty)
lambda.min.ratio	numeric	-		[0, 1]
lambda	untyped	NULL		-
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	-
relax	logical	FALSE	TRUE, FALSE	-
trace.it	integer	0		[0, 1]
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		[1, \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		[0, \infty)
pmax	integer	NULL		[0, \infty)
exact	logical	FALSE	TRUE, FALSE	-
s	numeric	0.01		[0, \infty)
gamma	numeric	1		[0, 1]
use_pred_offset	logical	-	TRUE, FALSE	-

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.

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.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGlmnet

Methods

Public methods

• LearnerClassifGlmnet$new()

• LearnerClassifGlmnet$selected_features()

• LearnerClassifGlmnet$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()

---

LearnerClassifGlmnet$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGlmnet$new()

---

LearnerClassifGlmnet$selected_features()

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

Usage

LearnerClassifGlmnet$selected_features(lambda = NULL)

Arguments

lambda

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

Returns

(character()) of feature names.

---

LearnerClassifGlmnet$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmnet$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.cv_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.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 June 9, 2026, 5:07 p.m.