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

Format

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

Construction

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