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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.aorsf")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, aorsf

Parameters

Id	Type	Default	Levels	Range
attach_data	logical	TRUE	TRUE, FALSE	-
epsilon	numeric	1e-09		\([0, \infty)\)
importance	character	anova	none, anova, negate, permute	-
importance_max_pvalue	numeric	0.01		\([1e-04, 0.9999]\)
leaf_min_events	integer	1		\([1, \infty)\)
leaf_min_obs	integer	5		\([1, \infty)\)
max_iter	integer	20		\([1, \infty)\)
method	character	glm	glm, net, pca, random	-
mtry	integer	NULL		\([1, \infty)\)
mtry_ratio	numeric	-		\([0, 1]\)
n_retry	integer	3		\([0, \infty)\)
n_split	integer	5		\([1, \infty)\)
n_thread	integer	-		\([0, \infty)\)
n_tree	integer	500		\([1, \infty)\)
na_action	character	fail	fail, impute_meanmode	-
net_mix	numeric	0.5		\((-\infty, \infty)\)
oobag	logical	FALSE	TRUE, FALSE	-
oobag_eval_every	integer	NULL		\([1, \infty)\)
oobag_fun	untyped	NULL		-
oobag_pred_type	character	mean	none, leaf, mean	-
pred_aggregate	logical	TRUE	TRUE, FALSE	-
sample_fraction	numeric	0.632		\([0, 1]\)
sample_with_replacement	logical	TRUE	TRUE, FALSE	-
scale_x	logical	FALSE	TRUE, FALSE	-
split_min_events	integer	5		\([1, \infty)\)
split_min_obs	integer	10		\([1, \infty)\)
split_min_stat	numeric	NULL		\([0, \infty)\)
split_rule	character	variance	variance	-
target_df	integer	NULL		\([1, \infty)\)
tree_seeds	integer	NULL		\([1, \infty)\)
verbose_progress	logical	FALSE	TRUE, FALSE	-

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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrObliqueRandomForest$new()

---

Method oob_error()

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerRegrObliqueRandomForest$oob_error()

Returns

numeric().

---

Method importance()

The importance scores are extracted from the model.

Usage

LearnerRegrObliqueRandomForest$importance()

Returns

Named numeric().

---

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")
print(learner)
#> 
#> ── <LearnerRegrObliqueRandomForest> (regr.aorsf): Oblique Random Forest Regresso
#> • Model: -
#> • Parameters: 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$model)
#> ---------- Oblique random regression forest
#> 
#>      Linear combinations: Linear regression
#>           N observations: 21
#>                  N trees: 500
#>       N predictors total: 10
#>    N predictors per node: 4
#>  Average leaves per tree: 3.256
#> Min observations in leaf: 5
#>           OOB stat value: 0.68
#>            OOB stat type: RSQ
#>      Variable importance: anova
#> 
#> -----------------------------------------
print(learner$importance())
#>        cyl       disp         wt         hp       qsec       gear       carb 
#> 0.14150943 0.10826211 0.08791209 0.05934718 0.05769231 0.03560831 0.03384615 
#>         vs         am       drat 
#> 0.02388060 0.02034884 0.01197605 

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

# Score the predictions
predictions$score()
#> regr.mse 
#> 13.29896