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

• Chapter in themlr3book:https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Packagemlr3extralearners for more learners.

• Dictionary ofLearners:mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of availableLearners 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,mlr3tuningspacesfor 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

Public methods

• LearnerClassifRanger$new()

• LearnerClassifRanger$importance()

• LearnerClassifRanger$oob_error()

• LearnerClassifRanger$selected_features()

• LearnerClassifRanger$clone()

Methodnew()

Creates a new instance of thisR6 class.

LearnerClassifRanger$new()

Methodimportance()

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

LearnerClassifRanger$importance()

Methodoob_error()

The out-of-bag error, extracted from model slotprediction.error.

LearnerClassifRanger$oob_error()

Methodselected_features()

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

LearnerClassifRanger$selected_features()

Methodclone()

The objects of this class are cloneable with this method.

LearnerClassifRanger$clone(deep=FALSE)

if(requireNamespace("ranger", quietly=TRUE)){# Define the Learner and set parameter valueslearner=lrn("classif.ranger")print(learner)# Define a Tasktask=tsk("sonar")# Create train and test setids=partition(task)# Train the learner on the training idslearner$train(task, row_ids=ids$train)# print the modelprint(learner$model)# importance methodif("importance"%in%learner$properties)print(learner$importance)# Make predictions for the test rowspredictions=learner$predict(task, row_ids=ids$test)# Score the predictionspredictions$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, missings, multiclass,#>   oob_error, selected_features, twoclass, weights#> Ranger result#>#> Call:#>  ranger::ranger(dependent.variable.name = task$target_names, data = task$data(),      probability = self$predict_type == "prob", 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:             19.42 %#> function ()#> .__LearnerClassifRanger__importance(self = self, private = private,#>     super = super)#> <environment: 0x559c03c87ad8>#> classif.ce#>   0.173913