mlr_learners_surv.coxtime.Rd
Calls survivalmodels::coxtime from package survivalmodels.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.coxtime") lrn("surv.coxtime")
Packages: survivalmodels, distr6, reticulate
Predict Types: crank, distr
Feature Types: integer, numeric
Properties:
Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCoxtime
new()
Creates a new instance of this R6 class.
LearnerSurvCoxtime$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvCoxtime$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.coxtime")) print(learner)#> <LearnerSurvCoxtime:surv.coxtime> #> * Model: - #> * Parameters: list() #> * Packages: survivalmodels, distr6, reticulate #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$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"