Regression Regularized Random Forest Learner

Regularized random forest for regression. Calls RRF::RRF() from RRF.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.RRF")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, RRF

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		\([1, \infty)\)
mtry	integer	-		\([1, \infty)\)
nodesize	integer	-		\([1, \infty)\)
replace	logical	TRUE	TRUE, FALSE	-
flagReg	integer	1		\([0, \infty)\)
coefReg	numeric	0.8		\((-\infty, \infty)\)
feaIni	untyped	-		-
corr.bias	logical	FALSE	TRUE, FALSE	-
maxnodes	integer	-		\([1, \infty)\)
importance	logical	FALSE	TRUE, FALSE	-
localImp	logical	FALSE	TRUE, FALSE	-
nPerm	integer	1		\([1, \infty)\)
proximity	logical	FALSE	TRUE, FALSE	-
oob.prox	logical	FALSE	TRUE, FALSE	-
do.trace	logical	FALSE	TRUE, FALSE	-
keep.inbag	logical	FALSE	TRUE, FALSE	-
keep.forest	logical	TRUE	TRUE, FALSE	-
strata	untyped	-		-
sampsize	untyped	-		-
predict.all	logical	FALSE	TRUE, FALSE	-
nodes	logical	FALSE	TRUE, FALSE	-

References

Deng, Houtao, Runger, George (2012). “Feature selection via regularized trees.” In 2012 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE. doi:10.1109/IJCNN.2012.6252640 .

Deng, Houtao, Runger, George (2013). “Gene selection with guided regularized random forest.” Pattern Recognition, 46(12), 3483–3489. doi:10.1016/j.patcog.2013.05.021 .

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

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRRF

Methods

Public methods

• LearnerRegrRRF$new()

• LearnerRegrRRF$importance()

• LearnerRegrRRF$oob_error()

• LearnerRegrRRF$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

LearnerRegrRRF$new()

---

Method importance()

The importance scores are extracted from the slot importance.

Usage

LearnerRegrRRF$importance()

Returns

Named numeric().

---

Method oob_error()

OOB errors are extracted from the model slot mse.

Usage

LearnerRegrRRF$oob_error()

Returns

numeric(1).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRRF$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.RRF")
print(learner)
#> 
#> ── <LearnerRegrRRF> (regr.RRF): Regularized Random Forest ──────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and RRF
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: importance and oob_error
#> • Other settings: use_weights = 'error'

# 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$model)
#> 
#> Call:
#>  RRF(formula = task$formula(), data = task$data()) 
#>                Type of random forest: regression
#>                      Number of trees: 500
#> No. of variables tried at each split: 3
#> 
#>           Mean of squared residuals: 7.789201
#>                     % Var explained: 81.25
print(learner$importance())
#>      disp        wt       cyl        hp      carb      drat      gear      qsec 
#> 206.70448 165.11618 138.55141 121.29424  56.86631  48.55964  24.26459  20.13651 
#>        vs        am 
#>  16.11049  11.93123 

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

# Score the predictions
predictions$score()
#> regr.mse 
#> 2.907525