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

The default for hyperparameter family is set to "cox".

Dictionary

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

mlr_learners$get("surv.cv_glmnet")
lrn("surv.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 -> mlr3proba::LearnerSurv -> LearnerSurvCVGlmnet

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCVGlmnet$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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