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

The default for hyperparameter family is changed 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")

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::LearnerRegr -> LearnerRegrCVGlmnet

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCVGlmnet$new()


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")) { learner = mlr3::lrn("regr.cv_glmnet") print(learner) # available parameters: learner$param_set$ids() }
#> <LearnerRegrCVGlmnet:regr.cv_glmnet> #> * Model: - #> * Parameters: family=gaussian #> * Packages: glmnet #> * Predict Type: response #> * Feature types: logical, integer, numeric #> * Properties: weights
#> [1] "family" "offset" "alpha" "nfolds" #> [5] "type.measure" "s" "lambda.min.ratio" "lambda" #> [9] "standardize" "intercept" "thresh" "dfmax" #> [13] "pmax" "exclude" "penalty.factor" "lower.limits" #> [17] "upper.limits" "maxit" "type.gaussian" "type.logistic" #> [21] "type.multinomial" "keep" "parallel" "trace.it" #> [25] "foldid" "alignment" "grouped" "gamma" #> [29] "relax" "fdev" "devmax" "eps" #> [33] "epsnr" "big" "mnlam" "pmin" #> [37] "exmx" "prec" "mxit" "mxitnr" #> [41] "newoffset" "predict.gamma"