mlr_learners_regr.cv_glmnet: GLM with Elastic Net Regularization Regression Learner

mlr_learners_regr.cv_glmnetR Documentation

GLM with Elastic Net Regularization Regression Learner

Description

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

The default for hyperparameter family is set to "gaussian".

Dictionary

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

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

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3, mlr3learners, glmnet

Parameters

Id Type Default Levels Range
alignment character lambda lambda, fraction -
alpha numeric 1 [0, 1]
big numeric 9.9e+35 (-\infty, \infty)
devmax numeric 0.999 [0, 1]
dfmax integer - [0, \infty)
eps numeric 1e-06 [0, 1]
epsnr numeric 1e-08 [0, 1]
exclude integer - [1, \infty)
exmx numeric 250 (-\infty, \infty)
family character gaussian gaussian, poisson -
fdev numeric 1e-05 [0, 1]
foldid untyped NULL -
gamma untyped - -
grouped logical TRUE TRUE, FALSE -
intercept logical TRUE TRUE, FALSE -
keep logical FALSE TRUE, FALSE -
lambda untyped - -
lambda.min.ratio numeric - [0, 1]
lower.limits untyped - -
maxit integer 100000 [1, \infty)
mnlam integer 5 [1, \infty)
mxit integer 100 [1, \infty)
mxitnr integer 25 [1, \infty)
nfolds integer 10 [3, \infty)
nlambda integer 100 [1, \infty)
use_pred_offset logical TRUE TRUE, FALSE -
parallel logical FALSE TRUE, FALSE -
penalty.factor untyped - -
pmax integer - [0, \infty)
pmin numeric 1e-09 [0, 1]
prec numeric 1e-10 (-\infty, \infty)
predict.gamma numeric gamma.1se (-\infty, \infty)
relax logical FALSE TRUE, FALSE -
s numeric lambda.1se [0, \infty)
standardize logical TRUE TRUE, FALSE -
standardize.response logical FALSE TRUE, FALSE -
thresh numeric 1e-07 [0, \infty)
trace.it integer 0 [0, 1]
type.gaussian character - covariance, naive -
type.logistic character - Newton, modified.Newton -
type.measure character deviance deviance, class, auc, mse, mae -
type.multinomial character - ungrouped, grouped -
upper.limits untyped - -

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::LearnerRegr -> LearnerRegrCVGlmnet

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerRegrCVGlmnet$new()

Method selected_features()

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

Usage
LearnerRegrCVGlmnet$selected_features(lambda = NULL)
Arguments
lambda

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

Returns

(character()) of feature names.


Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRegrCVGlmnet$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

Other Learner: mlr_learners_classif.cv_glmnet, 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.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

if (requireNamespace("glmnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.cv_glmnet")
print(learner)

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

# 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 April 3, 2025, 7:44 p.m.