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Accelerated oblique random survival forest. Calls aorsf::orsf() from aorsf. Note that although the learner has the property "missing" and it can in principle deal with missing values, the behaviour has to be configured using the parameter na_action.

Dictionary

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

mlr_learners$get("surv.aorsf")
lrn("surv.aorsf")

Meta Information

Parameters

IdTypeDefaultLevelsRange
n_treeinteger500\([1, \infty)\)
n_splitinteger5\([1, \infty)\)
n_retryinteger3\([0, \infty)\)
n_threadinteger1\([0, \infty)\)
pred_aggregatelogicalTRUETRUE, FALSE-
mtryintegerNULL\([1, \infty)\)
mtry_rationumeric-\([0, 1]\)
sample_with_replacementlogicalTRUETRUE, FALSE-
sample_fractionnumeric0.632\([0, 1]\)
control_typecharacterfastfast, cph, net-
split_rulecharacterlogranklogrank, cstat-
control_fast_do_scalelogicalTRUETRUE, FALSE-
control_fast_methodcharacterefronefron, breslow-
control_cph_methodcharacterefronefron, breslow-
control_cph_epsnumeric1e-09\([0, \infty)\)
control_cph_iter_maxinteger20\([1, \infty)\)
control_net_alphanumeric0.5\((-\infty, \infty)\)
control_net_df_targetintegerNULL\([1, \infty)\)
leaf_min_eventsinteger1\([1, \infty)\)
leaf_min_obsinteger5\([1, \infty)\)
split_min_eventsinteger5\([1, \infty)\)
split_min_obsinteger10\([1, \infty)\)
split_min_statnumeric3.841459\([0, \infty)\)
oobag_pred_typecharactersurvnone, surv, risk, chf-
importancecharacteranovanone, anova, negate, permute-
oobag_pred_horizonnumericNULL\([0, \infty)\)
oobag_eval_everyintegerNULL\([1, \infty)\)
attach_datalogicalTRUETRUE, FALSE-
verbose_progresslogicalFALSETRUE, FALSE-
na_actioncharacterfailfail, omit, impute_meanmode-

Initial parameter values

  • mtry:

    • This hyperparameter can alternatively be set via the added hyperparameter mtry_ratio as mtry = max(ceiling(mtry_ratio * n_features), 1). Note that mtry and mtry_ratio are mutually exclusive.

References

Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min Y, Mcclure LA, Howard G, Simon N (2019). “Oblique random survival forests.” The Annals of Applied Statistics, 13(3). doi:10.1214/19-aoas1261 .

Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM (2022). “Accelerated and interpretable oblique random survival forests.” arXiv preprint arXiv:2208.01129.

See also

Author

bcjaeger

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvAorsf

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerSurvAorsf$oob_error()

Returns

numeric().


Method importance()

The importance scores are extracted from the model.

Usage

LearnerSurvAorsf$importance()

Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvAorsf$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.aorsf")
print(learner)
#> <LearnerSurvAorsf:surv.aorsf>: Oblique Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, aorsf, pracma
#> * Predict Types:  [crank], distr
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, oob_error

# available parameters:
learner$param_set$ids()
#>  [1] "n_tree"                  "n_split"                
#>  [3] "n_retry"                 "n_thread"               
#>  [5] "pred_aggregate"          "pred_simplify"          
#>  [7] "mtry"                    "mtry_ratio"             
#>  [9] "sample_with_replacement" "sample_fraction"        
#> [11] "control_type"            "split_rule"             
#> [13] "control_fast_do_scale"   "control_fast_ties"      
#> [15] "control_cph_ties"        "control_cph_eps"        
#> [17] "control_cph_iter_max"    "control_net_alpha"      
#> [19] "control_net_df_target"   "leaf_min_events"        
#> [21] "leaf_min_obs"            "split_min_events"       
#> [23] "split_min_obs"           "split_min_stat"         
#> [25] "oobag_pred_type"         "importance"             
#> [27] "importance_max_pvalue"   "oobag_pred_horizon"     
#> [29] "oobag_eval_every"        "attach_data"            
#> [31] "verbose_progress"        "na_action"