Oblique Random Forest Regressor

Accelerated oblique random regression forest. Calls aorsf::orsf() from aorsf. Note that although the learner has the property "missing" and it can in principle deal with missing values, the behaviour has to be configured using the parameter na_action.

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::LearnerRegr -> LearnerRegrObliqueRandomForest

Methods

Public methods

• LearnerRegrObliqueRandomForest$new()

• LearnerRegrObliqueRandomForest$oob_error()

• LearnerRegrObliqueRandomForest$importance()

• LearnerRegrObliqueRandomForest$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$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrObliqueRandomForest$new()

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Method oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerRegrObliqueRandomForest$oob_error()

Returns

numeric().

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Method importance()

The importance scores are extracted from the model.

Usage

LearnerRegrObliqueRandomForest$importance()

Returns

Named numeric().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrObliqueRandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.aorsf", importance = "anova")
print(learner)
#> 
#> ── <LearnerRegrObliqueRandomForest> (regr.aorsf): Oblique Random Forest Regresso
#> • Model: -
#> • Parameters: importance=anova, n_thread=1
#> • Packages: mlr3, mlr3extralearners, and aorsf
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, oob_error, and weights
#> • Other settings: use_weights = 'use'

# 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(learner$model)
#> ---------- Oblique random regression forest
#> 
#>      Linear combinations: Linear regression
#>           N observations: 21
#>                  N trees: 500
#>       N predictors total: 10
#>    N predictors per node: 4
#>  Average leaves per tree: 3.252
#> Min observations in leaf: 5
#>           OOB stat value: 0.59
#>            OOB stat type: RSQ
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>         wt         hp       carb        cyl         am       drat       disp 
#> 0.16573034 0.05526316 0.04469274 0.03289474 0.02694611 0.02556818 0.02528090 
#>       qsec       gear         vs 
#> 0.02362205 0.02222222 0.01200000 

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

# Score the predictions
predictions$score()
#> regr.mse 
#> 11.58313