mlr_learners_surv.dnnsurv.Rd
Calls survivalmodels::dnnsurv from package survivalmodels.
Custom nets can be used in this learner either using the
survivalmodels::build_keras_net utility function or using keras.
The number of output channels should be of length 1
and number of input channels is
the number of features plus number of cuts.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.dnnsurv") lrn("surv.dnnsurv")
Packages: survivalmodels, keras, pseudo, tensorflow, distr6
Predict Types: crank, distr
Feature Types: integer, numeric
Properties:
verbose
:
Actual default: 1L
Adjusted default: 0L
Reason for change: Prevents plotting.
Zhao, L., & Feng, D. (2020). DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values. https://arxiv.org/abs/1908.02337
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvDNNSurv
new()
Creates a new instance of this R6 class.
LearnerSurvDNNSurv$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvDNNSurv$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.dnnsurv")) print(learner)#> <LearnerSurvDNNSurv:surv.dnnsurv> #> * Model: - #> * Parameters: verbose=0 #> * Packages: survivalmodels, keras, pseudo, tensorflow, distr6 #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$param_set$ids()#> [1] "cuts" "cutpoints" "custom_model" #> [4] "optimizer" "lr" "beta_1" #> [7] "beta_2" "epsilon" "decay" #> [10] "clipnorm" "clipvalue" "schedule_decay" #> [13] "momentum" "nesterov" "loss_weights" #> [16] "weighted_metrics" "early_stopping" "min_delta" #> [19] "patience" "verbose" "baseline" #> [22] "restore_best_weights" "batch_size" "epochs" #> [25] "validation_split" "shuffle" "sample_weight" #> [28] "initial_epoch" "steps_per_epoch" "validation_steps" #> [31] "steps" "callbacks"