mlr_learners_surv.obliqueRSF: Survival Oblique Random Survival Forest Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Oblique Random Survival Forest Learner

Description

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 Note that obliqueRSF has been superseded by aorsf.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

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()

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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.

Author(s)

adibender

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). \Sexpr[results=rd]{tools:::Rd_expr_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.

Examples

learner = mlr3::lrn("surv.obliqueRSF")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.