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: 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.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::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
always.split.variables	untyped	-		-
class.weights	untyped	NULL		-
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	untyped	1L		-
min.node.size	untyped	NULL		-
mtry	integer	-		\([1, \infty)\)
mtry.ratio	numeric	-		\([0, 1]\)
na.action	character	na.learn	na.learn, na.omit, na.fail	-
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, order, partition	-
sample.fraction	numeric	-		\([0, 1]\)
save.memory	logical	FALSE	TRUE, FALSE	-
scale.permutation.importance	logical	FALSE	TRUE, FALSE	-
local.importance	logical	FALSE	TRUE, FALSE	-
seed	integer	NULL		\((-\infty, \infty)\)
split.select.weights	untyped	NULL		-
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: 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.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$selected_features()

• LearnerClassifRanger$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• 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()
• mlr3::LearnerClassif$predict_newdata_fast()

---

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 selected_features()

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

Usage

LearnerClassifRanger$selected_features()

Returns

character().

---

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

# Define the Learner and set parameter values
learner = lrn("classif.ranger")
learner$param_set$set_values(importance = "permutation")
print(learner)
#> 
#> ── <LearnerClassifRanger> (classif.ranger): Random Forest ──────────────────────
#> • Model: -
#> • Parameters: importance=permutation, num.threads=1
#> • Packages: mlr3, mlr3learners, and ranger
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, character, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: hotstart_backward, importance, missings, multiclass, oob_error,
#> selected_features, twoclass, and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'

# 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$model)
#> Ranger result
#> 
#> Call:
#>  ranger::ranger(dependent.variable.name = task$target_names, data = task$data(),      probability = self$predict_type == "prob", importance = "permutation",      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:         permutation 
#> Splitrule:                        gini 
#> OOB prediction error:             19.42 % 

# Importance method
print(learner$importance())
#>           V12           V11           V10            V9           V36 
#>  3.337599e-02  2.616688e-02  1.624741e-02  1.267319e-02  1.266523e-02 
#>           V49           V35           V37           V48           V51 
#>  9.591239e-03  8.398474e-03  8.011852e-03  6.791566e-03  6.429335e-03 
#>           V47           V46            V5           V45           V13 
#>  5.677990e-03  5.196811e-03  5.063672e-03  5.059326e-03  4.602567e-03 
#>           V34           V21           V28           V16            V8 
#>  4.510314e-03  4.251763e-03  4.227416e-03  4.196607e-03  3.784386e-03 
#>            V4           V26           V22           V23           V31 
#>  3.073572e-03  2.800287e-03  2.762688e-03  2.617181e-03  2.560049e-03 
#>           V18           V15            V2           V27           V44 
#>  2.298490e-03  2.274618e-03  2.217058e-03  2.123583e-03  2.066563e-03 
#>           V30            V1           V24           V52           V38 
#>  1.738636e-03  1.713017e-03  1.664384e-03  1.490453e-03  1.475323e-03 
#>           V17           V29           V32           V33            V6 
#>  1.353299e-03  1.321075e-03  1.313594e-03  1.173741e-03  1.161417e-03 
#>           V56           V54           V42           V43           V14 
#>  1.094181e-03  9.343498e-04  8.589654e-04  7.394226e-04  6.846147e-04 
#>            V3           V57           V25           V59           V53 
#>  5.795359e-04  5.719396e-04  5.257730e-04  3.131463e-04  2.842464e-04 
#>           V55           V40            V7           V39           V20 
#>  2.524405e-04  2.439645e-04  2.408485e-04  2.336106e-04  1.872417e-04 
#>           V19           V60           V41           V58           V50 
#>  2.669592e-05  2.124530e-05  1.185959e-05 -3.304456e-04 -4.521934e-04 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>   0.173913