mlr_learners_surv.deepsurv.Rd
Calls survivalmodels::deepsurv from package survivalmodels.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.deepsurv") lrn("surv.deepsurv")
Packages: survivalmodels, distr6, reticulate
Predict Types: crank, distr
Feature Types: integer, numeric
Properties:
Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvDeepsurv
new()
Creates a new instance of this R6 class.
LearnerSurvDeepsurv$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvDeepsurv$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.deepsurv")) print(learner)#> <LearnerSurvDeepsurv:surv.deepsurv> #> * Model: - #> * Parameters: list() #> * Packages: survivalmodels, distr6, reticulate #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$param_set$ids()#> [1] "frac" "num_nodes" "batch_norm" "dropout" #> [5] "activation" "device" "shrink" "optimizer" #> [9] "rho" "eps" "lr" "weight_decay" #> [13] "learning_rate" "lr_decay" "betas" "amsgrad" #> [17] "lambd" "alpha" "t0" "momentum" #> [21] "centered" "etas" "step_sizes" "dampening" #> [25] "nesterov" "batch_size" "epochs" "verbose" #> [29] "num_workers" "shuffle" "best_weights" "early_stopping" #> [33] "min_delta" "patience"