Dictionary

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

mlr_learners$get("surv.coxtime")
lrn("surv.coxtime")

Traits

  • Packages: survivalmodels, distr6, reticulate

  • Predict Types: crank, distr

  • Feature Types: integer, numeric

  • Properties:

References

Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxtime

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxtime$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxtime$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.coxtime")) print(learner)
#> <LearnerSurvCoxtime:surv.coxtime> #> * Model: - #> * Parameters: list() #> * Packages: survivalmodels, distr6, reticulate #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "frac" "standardize_time" "log_duration" "with_mean" #> [5] "with_std" "num_nodes" "batch_norm" "dropout" #> [9] "activation" "device" "shrink" "optimizer" #> [13] "rho" "eps" "lr" "weight_decay" #> [17] "learning_rate" "lr_decay" "betas" "amsgrad" #> [21] "lambd" "alpha" "t0" "momentum" #> [25] "centered" "etas" "step_sizes" "dampening" #> [29] "nesterov" "batch_size" "epochs" "verbose" #> [33] "num_workers" "shuffle" "best_weights" "early_stopping" #> [37] "min_delta" "patience"