Calls glmnet::glmnet().

Use LearnerSurvGlmnet and LearnerSurvCVGlmnet for glmnets without and with internal cross-validation, respectively. Tuning using the internal optimizer in LearnerSurvCVGlmnet may be more efficient when tuning lambda only. However, for tuning multiple hyperparameters, mlr3tuning and LearnerSurvGlmnet will likely give better results.

Parameter s (value of the regularization parameter used for predictions) is set to the median of the lambda sequence by default, but needs to be tuned by the user.

Dictionary

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

LearnerSurvGlmnet$new()
mlr_learners$get("surv.glmnet")
lrn("surv.glmnet")

Meta Information

  • Type: "surv"

  • Predict Types: crank, lp

  • Feature Types: integer, numeric, factor

  • Properties: weights

  • Packages: glmnet survival

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1). doi: 10.18637/jss.v033.i01 .

See also

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvGlmnet$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.