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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")

Meta Information

Parameters

IdTypeDefaultLevelsRange
fracnumeric0\([0, 1]\)
standardize_timelogicalFALSETRUE, FALSE\((-\infty, \infty)\)
log_durationlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
with_meanlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
with_stdlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
num_nodeslist32, 32\((-\infty, \infty)\)
batch_normlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
dropoutnumeric-\([0, 1]\)
activationcharacterrelucelu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...\((-\infty, \infty)\)
devicelist-\((-\infty, \infty)\)
shrinknumeric0\([0, \infty)\)
optimizercharacteradamadadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam\((-\infty, \infty)\)
rhonumeric0.9\((-\infty, \infty)\)
epsnumeric1e-08\((-\infty, \infty)\)
lrnumeric1\((-\infty, \infty)\)
weight_decaynumeric0\((-\infty, \infty)\)
learning_ratenumeric0.01\((-\infty, \infty)\)
lr_decaynumeric0\((-\infty, \infty)\)
betaslist0.900, 0.999\((-\infty, \infty)\)
amsgradlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
lambdnumeric1e-04\([0, \infty)\)
alphanumeric0.75\([0, \infty)\)
t0numeric1e+06\((-\infty, \infty)\)
momentumnumeric0\((-\infty, \infty)\)
centeredlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
etaslist0.5, 1.2\((-\infty, \infty)\)
step_sizeslist1e-06, 5e+01\((-\infty, \infty)\)
dampeningnumeric0\((-\infty, \infty)\)
nesterovlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
batch_sizeinteger256\((-\infty, \infty)\)
epochsinteger1\([1, \infty)\)
verboselogicalTRUETRUE, FALSE\((-\infty, \infty)\)
num_workersinteger0\((-\infty, \infty)\)
shufflelogicalTRUETRUE, FALSE\((-\infty, \infty)\)
best_weightslogicalFALSETRUE, FALSE\((-\infty, \infty)\)
early_stoppinglogicalFALSETRUE, FALSE\((-\infty, \infty)\)
min_deltanumeric0\((-\infty, \infty)\)
patienceinteger10\((-\infty, \infty)\)

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

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

LearnerSurvCoxtime$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("survivalmodels", quietly = TRUE) && requireNamespace("distr6", quietly = TRUE) && requireNamespace("reticulate", quietly = TRUE)) {
  learner = mlr3::lrn("surv.coxtime")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvCoxtime:surv.coxtime>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, survivalmodels, distr6, reticulate
#> * Predict Type: crank
#> * Feature types: integer, numeric
#> * Properties: -
#>  [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"