Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet. Hyperparameter family is set 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" "alpha" "nfolds" "type.measure" #> [5] "s" "nlambda" "lambda.min.ratio" "lambda" #> [9] "standardize" "intercept" "thresh" "dfmax" #> [13] "pmax" "exclude" "penalty.factor" "lower.limits" #> [17] "upper.limits" "maxit" "type.logistic" "type.multinomial" #> [21] "fdev" "devmax" "eps" "big" #> [25] "mnlam" "pmin" "exmx" "prec" #> [29] "mxit"