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.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

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

Meta Information

  • Type: "surv"

  • Predict Types: crank, lp

  • Feature Types: integer, numeric, factor

  • 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