Survival Oblique Random Survival Forest Learner

Oblique random forest. Calls obliqueRSF::ORSF() from obliqueRSF. Note that obliqueRSF has been superseded by aorsf. We highly recommend you use aorsf to fit oblique random survival forests: see https://github.com/bcjaeger/aorsf or install from CRAN with install.packages('aorsf').

Dictionary

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

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

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, obliqueRSF, pracma

Parameters

Id	Type	Default	Levels	Range
alpha	numeric	0.5		\((-\infty, \infty)\)
ntree	integer	100		\([1, \infty)\)
eval_times	untyped	-		-
min_events_to_split_node	integer	5		\([1, \infty)\)
min_obs_to_split_node	integer	10		\([1, \infty)\)
min_obs_in_leaf_node	integer	5		\([1, \infty)\)
min_events_in_leaf_node	integer	1		\([1, \infty)\)
nsplit	integer	25		\([1, \infty)\)
gamma	numeric	0.5		\([1e-16, \infty)\)
max_pval_to_split_node	numeric	0.5		\([0, 1]\)
mtry	integer	-		\([1, \infty)\)
mtry_ratio	numeric	-		\([0, 1]\)
dfmax	integer	-		\([1, \infty)\)
use.cv	logical	FALSE	TRUE, FALSE	-
verbose	logical	TRUE	TRUE, FALSE	-
compute_oob_predictions	logical	FALSE	TRUE, FALSE	-
random_seed	integer	-		\((-\infty, \infty)\)

Custom mlr3 parameters

• 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.

Initial parameter values

• verbose is initialized to FALSE

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 .

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

adibender

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvObliqueRSF

Methods

Public methods

• LearnerSurvObliqueRSF$new()

• LearnerSurvObliqueRSF$oob_error()

• LearnerSurvObliqueRSF$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvObliqueRSF$new()

---

Method oob_error()

Integrated brier score OOB error extracted from the model slot oob_error. Concordance is also available.

Usage

LearnerSurvObliqueRSF$oob_error()

Returns

numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvObliqueRSF$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# available parameters:
learner$param_set$ids()
#>  [1] "alpha"                    "ntree"                   
#>  [3] "eval_times"               "min_events_to_split_node"
#>  [5] "min_obs_to_split_node"    "min_obs_in_leaf_node"    
#>  [7] "min_events_in_leaf_node"  "nsplit"                  
#>  [9] "gamma"                    "max_pval_to_split_node"  
#> [11] "mtry"                     "mtry_ratio"              
#> [13] "dfmax"                    "use.cv"                  
#> [15] "verbose"                  "compute_oob_predictions" 
#> [17] "random_seed"