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Penalized (L1 and L2) generalized linear models. Calls penalized::penalized() from penalized.

Details

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.

Dictionary

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")

Meta Information

Parameters

IdTypeDefaultLevelsRange
unpenalizeduntyped--
lambda1untyped0-
lambda2untyped0-
positivelogicalFALSETRUE, FALSE-
fusedllogicalFALSETRUE, FALSE-
startbetanumeric-\((-\infty, \infty)\)
startgammanumeric-\((-\infty, \infty)\)
stepsinteger1\([1, \infty)\)
epsilonnumeric1e-10\([0, 1]\)
maxiterinteger-\([1, \infty)\)
standardizelogicalFALSETRUE, FALSE-
tracelogicalTRUETRUE, FALSE-

References

Goeman, J J (2010). “L1 penalized estimation in the Cox proportional hazards model.” Biometrical journal, 52(1), 70--84.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPenalized

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.penalized")
print(learner)
#> <LearnerSurvPenalized:surv.penalized>: Penalized Regression
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, penalized, pracma
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor, logical
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "unpenalized" "lambda1"     "lambda2"     "positive"    "fusedl"     
#>  [6] "startbeta"   "startgamma"  "steps"       "epsilon"     "maxiter"    
#> [11] "standardize" "trace"