Oblique Random Forest Classifier

Accelerated oblique random classification forest. Calls aorsf::orsf() from aorsf.

Initial parameter values

• n_thread: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• pred_simplify has to be TRUE, otherwise response is NA in prediction

See also

• 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).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Author

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifObliqueRandomForest

Methods

Public methods

• LearnerClassifObliqueRandomForest$new()

• LearnerClassifObliqueRandomForest$oob_error()

• LearnerClassifObliqueRandomForest$importance()

• LearnerClassifObliqueRandomForest$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$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifObliqueRandomForest$new()

---

Method oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerClassifObliqueRandomForest$oob_error()

Returns

numeric().

---

Method importance()

The importance scores are extracted from the model.

Usage

LearnerClassifObliqueRandomForest$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifObliqueRandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.aorsf", importance = "anova")
print(learner)
#> <LearnerClassifObliqueRandomForest:classif.aorsf>: Oblique Random Forest Classifier
#> * Model: -
#> * Parameters: importance=anova, n_thread=1
#> * Packages: mlr3, mlr3extralearners, aorsf
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, multiclass, oob_error, twoclass

# Define a Task
task = mlr3::tsk("breast_cancer")
# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)
#> ---------- Oblique random classification forest
#> 
#>      Linear combinations: Logistic regression
#>           N observations: 458
#>                N classes: 2
#>                  N trees: 500
#>       N predictors total: 9
#>    N predictors per node: 3
#>  Average leaves per tree: 2.964
#> Min observations in leaf: 5
#>           OOB stat value: 0.99
#>            OOB stat type: AUC-ROC
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>     bare_nuclei    cl_thickness      cell_shape     bl_cromatin       cell_size 
#>       0.5716273       0.5369030       0.5317680       0.5219858       0.4333801 
#> normal_nucleoli   marg_adhesion    epith_c_size         mitoses 
#>       0.4226519       0.4159544       0.3222892       0.2561728 

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

# Score the predictions
predictions$score()
#> classif.ce 
#> 0.02666667