GLM with Elastic Net Regularization Regression Learner

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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function 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			-
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		\([1, \infty)\)
offset	untyped			-
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	-		-

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. 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: 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.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

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCVGlmnet

Methods

Public methods

• LearnerRegrCVGlmnet$new()

• LearnerRegrCVGlmnet$selected_features()

• LearnerRegrCVGlmnet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

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.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  learner = mlr3::lrn("regr.cv_glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrCVGlmnet:regr.cv_glmnet>: GLM with Elastic Net Regularization
#> * Model: -
#> * Parameters: family=gaussian
#> * Packages: mlr3, mlr3learners, glmnet
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights
#>  [1] "alignment"            "alpha"                "big"                 
#>  [4] "devmax"               "dfmax"                "eps"                 
#>  [7] "epsnr"                "exclude"              "exmx"                
#> [10] "family"               "fdev"                 "foldid"              
#> [13] "gamma"                "grouped"              "intercept"           
#> [16] "keep"                 "lambda"               "lambda.min.ratio"    
#> [19] "lower.limits"         "maxit"                "mnlam"               
#> [22] "mxit"                 "mxitnr"               "nfolds"              
#> [25] "nlambda"              "offset"               "parallel"            
#> [28] "penalty.factor"       "pmax"                 "pmin"                
#> [31] "prec"                 "predict.gamma"        "relax"               
#> [34] "s"                    "standardize"          "standardize.response"
#> [37] "thresh"               "trace.it"             "type.gaussian"       
#> [40] "type.logistic"        "type.measure"         "type.multinomial"    
#> [43] "upper.limits"