Ranger Survival Learner

Random survival forest. Calls ranger::ranger() from package ranger.

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 ranger::predict.ranger() function.

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

Custom mlr3 parameters

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

Initial parameter values

• num.threads is initialized to 1 to avoid conflicts with parallelization via future.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.ranger")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, ranger

Parameters

Id	Type	Default	Levels	Range
alpha	numeric	0.5		\((-\infty, \infty)\)
always.split.variables	untyped	-		-
holdout	logical	FALSE	TRUE, FALSE	-
importance	character	-	none, impurity, impurity_corrected, permutation	-
keep.inbag	logical	FALSE	TRUE, FALSE	-
max.depth	integer	NULL		\([0, \infty)\)
min.node.size	integer	5		\([1, \infty)\)
minprop	numeric	0.1		\((-\infty, \infty)\)
mtry	integer	-		\([1, \infty)\)
mtry.ratio	numeric	-		\([0, 1]\)
num.random.splits	integer	1		\([1, \infty)\)
num.threads	integer	1		\([1, \infty)\)
num.trees	integer	500		\([1, \infty)\)
oob.error	logical	TRUE	TRUE, FALSE	-
regularization.factor	untyped	1		-
regularization.usedepth	logical	FALSE	TRUE, FALSE	-
replace	logical	TRUE	TRUE, FALSE	-
respect.unordered.factors	character	ignore	ignore, order, partition	-
sample.fraction	numeric	-		\([0, 1]\)
save.memory	logical	FALSE	TRUE, FALSE	-
scale.permutation.importance	logical	FALSE	TRUE, FALSE	-
seed	integer	NULL		\((-\infty, \infty)\)
split.select.weights	numeric	-		\([0, 1]\)
splitrule	character	logrank	logrank, extratrees, C, maxstat	-
verbose	logical	TRUE	TRUE, FALSE	-
write.forest	logical	TRUE	TRUE, FALSE	-
min.bucket	integer	3		\((-\infty, \infty)\)
time.interest	integer	NULL		\([1, \infty)\)
node.stats	logical	FALSE	TRUE, FALSE	-

References

Wright, N. M, Ziegler, Andreas (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01 .

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, 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.

Author

be-marc

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRanger

Methods

Public methods

• LearnerSurvRanger$new()

• LearnerSurvRanger$importance()

• LearnerSurvRanger$oob_error()

• LearnerSurvRanger$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

LearnerSurvRanger$new()

---

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvRanger$importance()

Returns

Named numeric().

---

Method oob_error()

The out-of-bag error is extracted from the model slot prediction.error.

Usage

LearnerSurvRanger$oob_error()

Returns

numeric(1).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("surv.ranger", importance = "permutation")
print(learner)
#> <LearnerSurvRanger:surv.ranger>: Random Forest
#> * Model: -
#> * Parameters: importance=permutation, num.threads=1
#> * Packages: mlr3, mlr3proba, mlr3extralearners, ranger
#> * Predict Types:  [crank], distr
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: importance, oob_error, weights

# 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$model)
#> Ranger result
#> 
#> Call:
#>  ranger::ranger(formula = NULL, dependent.variable.name = targets[1L],      status.variable.name = targets[2L], data = task$data(), case.weights = task$weights$weight,      importance = "permutation", num.threads = 1L) 
#> 
#> Type:                             Survival 
#> Number of trees:                  500 
#> Sample size:                      670 
#> Number of independent variables:  6 
#> Mtry:                             3 
#> Target node size:                 3 
#> Variable importance mode:         permutation 
#> Splitrule:                        logrank 
#> Number of unique death times:     86 
#> OOB prediction error (1-C):       0.1502962 
print(learner$importance())
#>  revascdays      revasc         age         los       sysbp    stchange 
#> 0.152868170 0.100966614 0.043634712 0.021014789 0.018422314 0.003527928 

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

# Score the predictions
predictions$score()
#> surv.cindex 
#>   0.8199829