mlr_learners_regr.ranger {mlr3learners}

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”

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

• Required Packages: mlr3, mlr3learners, 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.bucket	integer	1		[1, \infty)
min.node.size	integer	5		[1, \infty)
minprop	numeric	0.1		(-\infty, \infty)
mtry	integer	-		[1, \infty)
mtry.ratio	numeric	-		[0, 1]
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	-
quantreg	logical	FALSE	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	-
se.method	character	infjack	jack, infjack	-
seed	integer	NULL		(-\infty, \infty)
split.select.weights	untyped	NULL		-
splitrule	character	variance	variance, extratrees, maxstat	-
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: NULL, triggering auto-detection of the number of CPUs.

  • Adjusted value: 1.

  • Reason for change: Conflicting with parallelization via future.

Super classes

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

Methods

Public methods

• LearnerRegrRanger$new()

• LearnerRegrRanger$importance()

• LearnerRegrRanger$oob_error()

• LearnerRegrRanger$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

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 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. 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

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• 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).

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

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

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

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()
}