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

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

Format

R6::R6Class() inheriting from mlr3::LearnerRegr.

Construction

LearnerRegrGlmnet$new()
mlr3::mlr_learners$get("regr.glmnet")
mlr3::lrn("regr.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

Examples

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