mlr_learners_surv.penalized | R Documentation |
Penalized (L1 and L2) generalized linear models.
Calls penalized::penalized()
from penalized.
The penalized
and unpenalized
arguments in the learner are implemented slightly
differently than in penalized::penalized()
. Here, there is no parameter for penalized
but
instead it is assumed that every variable is penalized unless stated in the unpenalized
parameter, see examples.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.penalized") lrn("surv.penalized")
Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, penalized, pracma
Id | Type | Default | Levels | Range |
unpenalized | untyped | - | - | |
lambda1 | untyped | 0 | - | |
lambda2 | untyped | 0 | - | |
positive | logical | FALSE | TRUE, FALSE | - |
fusedl | logical | FALSE | TRUE, FALSE | - |
startbeta | numeric | - | (-\infty, \infty) |
|
startgamma | numeric | - | (-\infty, \infty) |
|
steps | integer | 1 | [1, \infty) |
|
epsilon | numeric | 1e-10 | [0, 1] |
|
maxiter | integer | - | [1, \infty) |
|
standardize | logical | FALSE | TRUE, FALSE | - |
trace | logical | TRUE | TRUE, FALSE | - |
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvPenalized
new()
Creates a new instance of this R6 class.
LearnerSurvPenalized$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvPenalized$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Goeman, J J (2010). “L1 penalized estimation in the Cox proportional hazards model.” Biometrical journal, 52(1), 70–84.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("surv.penalized")
print(learner)
# available parameters:
learner$param_set$ids()
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