Ranger Classification Learner

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

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.

Dictionary

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

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

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• 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	-		-
class.weights	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	NULL		\([1, \infty)\)
minprop	numeric	0.1		\((-\infty, \infty)\)
mtry	integer	-		\([1, \infty)\)
mtry.ratio	numeric	-		\([0, 1]\)
num.random.splits	integer	1		\([1, \infty)\)
node.stats	logical	FALSE	TRUE, FALSE	-
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	-
se.method	character	infjack	jack, infjack	-
seed	integer	NULL		\((-\infty, \infty)\)
split.select.weights	untyped			-
splitrule	character	gini	gini, extratrees, hellinger	-
verbose	logical	TRUE	TRUE, FALSE	-
write.forest	logical	TRUE	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

• 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: 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.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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRanger

Methods

Public methods

• LearnerClassifRanger$new()

• LearnerClassifRanger$importance()

• LearnerClassifRanger$oob_error()

• LearnerClassifRanger$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

LearnerClassifRanger$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

LearnerClassifRanger$importance()

Returns

Named numeric().

---

Method oob_error()

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

Usage

LearnerClassifRanger$oob_error()

Returns

numeric(1).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

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

# 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$score()
}
#> <LearnerClassifRanger:classif.ranger>: Random Forest
#> * Model: -
#> * Parameters: num.threads=1
#> * Packages: mlr3, mlr3learners, ranger
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: hotstart_backward, importance, multiclass, oob_error,
#>   twoclass, weights
#> Ranger result
#> 
#> Call:
#>  ranger::ranger(dependent.variable.name = task$target_names, data = task$data(),      probability = self$predict_type == "prob", case.weights = task$weights$weight,      num.threads = 1L) 
#> 
#> Type:                             Classification 
#> Number of trees:                  500 
#> Sample size:                      139 
#> Number of independent variables:  60 
#> Mtry:                             7 
#> Target node size:                 1 
#> Variable importance mode:         none 
#> Splitrule:                        gini 
#> OOB prediction error:             17.99 % 
#> function () 
#> .__LearnerClassifRanger__importance(self = self, private = private, 
#>     super = super)
#> <environment: 0x5561a2e5c740>
#> classif.ce 
#>  0.1884058