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

Dictionary

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

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

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

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGlmnet$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.rpart") print(learner)
#> <LearnerRegrRpart:regr.rpart> #> * Model: - #> * Parameters: xval=0 #> * Packages: rpart #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, selected_features, weights
# available parameters: learner$param_set$ids()
#> [1] "minsplit" "minbucket" "cp" "maxcompete" #> [5] "maxsurrogate" "maxdepth" "usesurrogate" "surrogatestyle" #> [9] "xval"