mlr_learners_surv.rfsrc: Survival Random Forest SRC Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Random Forest SRC Learner

Description

Random survival forest. Calls randomForestSRC::rfsrc() from randomForestSRC.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internal randomForestSRC::predict.rfsrc() function.

2. crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.rfsrc")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

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

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

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		[1, \infty)
mtry	integer	-		[1, \infty)
mtry.ratio	numeric	-		[0, 1]
nodesize	integer	15		[1, \infty)
nodedepth	integer	-		[1, \infty)
splitrule	character	logrank	logrank, bs.gradient, logrankscore	-
nsplit	integer	10		[0, \infty)
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		[1, \infty)
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		[0, 1]
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		[1, \infty)
ntime	integer	150		[0, \infty)
cause	integer	-		[1, \infty)
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		(-\infty, -1]
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		[0, \infty)
estimator	character	nelson	nelson, kaplan	-
cores	integer	1		[1, \infty)
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	none	-
case.depth	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

• estimator: Hidden parameter that controls the type of estimator used to derive the survival function during prediction. The default value is "chf" which uses a bootstrapped Nelson-Aalen estimator for the cumulative hazard function H(t), (Ishwaran, 2008) from which we calculate S(t) = \exp(-H(t)), whereas "surv" uses a bootstrapped Kaplan-Meier estimator to directly estimate S(t).

• 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.

• sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.

• cores: This value is set as the option rf.cores during training and is set to 1 by default.

Initial parameter values

• ntime: Number of time points to coerce the observed event times for use in the estimated survival function during prediction. We changed the default value of 150 to 0 in order to be in line with other random survival forest learners and use all the unique event times from the train set.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRandomForestSRC

Methods

Public methods

• LearnerSurvRandomForestSRC$new()

• LearnerSurvRandomForestSRC$importance()

• LearnerSurvRandomForestSRC$selected_features()

• LearnerSurvRandomForestSRC$oob_error()

• LearnerSurvRandomForestSRC$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerSurvRandomForestSRC$new()

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Method importance()

The importance scores are extracted from the model slot importance.

Usage

LearnerSurvRandomForestSRC$importance()

Returns

Named numeric().

---

Method selected_features()

Selected features are extracted from the model slot var.used.

Usage

LearnerSurvRandomForestSRC$selected_features()

Returns

character().

---

Method oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerSurvRandomForestSRC$oob_error()

Returns

numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRandomForestSRC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008). “Random survival forests.” The Annals of Applied Statistics, 2(3). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/08-aoas169")}, https://doi.org/10.1214/08-aoas169.

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1023/A:1010933404324")}.

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

# Define the Learner
learner = mlr3::lrn("surv.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.