## Details

Four possible SVMs can be implemented, dependent on the type parameter. These correspond to predicting the survival time via regression (regression), predicting a continuous rank (vanbelle1, vanbelle2), or a hybrid of the two (hybrid). Whichever type is chosen determines how the crank predict type is calculated, but in any case all can be considered a valid continuous ranking.

makediff3 is recommended when using type = "hybrid".

## Dictionary

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

### Method clone()

The objects of this class are cloneable with this method.

LearnerSurvSVM$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples # stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.svm")) print(learner) #> <LearnerSurvSVM:surv.svm> #> * Model: - #> * Parameters: list() #> * Packages: survivalsvm #> * Predict Type: crank #> * Feature types: integer, numeric #> * Properties: - # available parameters: learner$param_set\$ids()
#>  [1] "type"        "diff.meth"   "gamma.mu"    "opt.meth"    "kernel"
#>  [6] "kernel.pars" "sgf.sv"      "sigf"        "maxiter"     "margin"
#> [11] "bound"       "eig.tol"     "conv.tol"    "posd.tol"