mlr_learners_regr.ranger: Ranger Regression Learner

mlr_learners_regr.rangerR Documentation

Ranger Regression Learner

Description

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

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.ranger")
lrn("regr.ranger")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”, “se”, “quantiles”

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

  • Required Packages: mlr3, mlr3learners, ranger

Parameters

Id Type Default Levels Range
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 [1, \infty)
min.bucket integer 1 [1, \infty)
min.node.size integer 5 [1, \infty)
mtry integer - [1, \infty)
mtry.ratio numeric - [0, 1]
na.action character na.learn na.learn, na.omit, na.fail -
node.stats logical FALSE TRUE, FALSE -
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 -
poisson.tau numeric 1 (-\infty, \infty)
regularization.factor untyped 1 -
regularization.usedepth logical FALSE TRUE, FALSE -
replace logical TRUE TRUE, FALSE -
respect.unordered.factors character - ignore, order, partition -
sample.fraction numeric - [0, 1]
save.memory logical FALSE TRUE, FALSE -
scale.permutation.importance logical FALSE TRUE, FALSE -
se.method character infjack jack, infjack -
seed integer NULL (-\infty, \infty)
split.select.weights untyped NULL -
splitrule character variance variance, extratrees, maxstat, beta, poisson -
verbose logical TRUE TRUE, FALSE -
write.forest logical TRUE TRUE, FALSE -

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:

    • Actual default: 2, using two threads, while also respecting environment variable R_RANGER_NUM_THREADS, options(ranger.num.threads = N), or options(Ncpus = N), with precedence in that order.

    • Adjusted value: 1.

    • Reason for change: Conflicting with parallelization via future.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRanger

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerRegrRanger$new()

Method importance()

The importance scores are extracted from the model slot variable.importance. Parameter importance.mode must be set to "impurity", "impurity_corrected", or "permutation"

Usage
LearnerRegrRanger$importance()
Returns

Named numeric().


Method oob_error()

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

Usage
LearnerRegrRanger$oob_error()
Returns

numeric(1).


Method selected_features()

The set of features used for node splitting in the forest.

Usage
LearnerRegrRanger$selected_features()
Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRegrRanger$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v077.i01")}.

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

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.ranger")
print(learner)

# Define a Task
task = tsk("mtcars")

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

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

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

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

# Score the predictions
predictions$score()
}

mlr3learners documentation built on April 3, 2025, 7:44 p.m.