Regression Random Forest SRC Learner

Random forest for regression. Calls randomForestSRC::rfsrc() from randomForestSRC.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.rfsrc")
lrn("regr.rfsrc")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, randomForestSRC

Parameters

Id	Type	Default	Levels	Range
ntree	integer	1000		\([1, \infty)\)
mtry	integer	-		\([1, \infty)\)
mtry.ratio	numeric	-		\([0, 1]\)
nodesize	integer	15		\([1, \infty)\)
nodedepth	integer	-		\([1, \infty)\)
splitrule	character	mse	mse, quantile.regr, la.quantile.regr	-
nsplit	integer	10		\([0, \infty)\)
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		\([1, \infty)\)
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		\([0, 1]\)
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		\([1, \infty)\)
ntime	integer	-		\([1, \infty)\)
cause	integer	-		\([1, \infty)\)
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		\((-\infty, -1]\)
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		\([0, \infty)\)
cores	integer	1		\([1, \infty)\)
save.memory	logical	FALSE	TRUE, FALSE	-

Parameter Changes

• cores:

  • Actual default: Auto-detecting the number of cores

  • Adjusted default: 1

  • Reason for change: Threading conflicts with explicit parallelization via future.

• mtry:

  • This hyperparameter can alternatively be set via the added hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.

• sampsize:

  • This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5--32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .

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

RaphaelS1

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForestSRC

Methods

Public methods

• LearnerRegrRandomForestSRC$new()

• LearnerRegrRandomForestSRC$importance()

• LearnerRegrRandomForestSRC$selected_features()

• LearnerRegrRandomForestSRC$oob_error()

• LearnerRegrRandomForestSRC$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomForestSRC$new()

---

Method importance()

The importance scores are extracted from the model slot importance.

Usage

LearnerRegrRandomForestSRC$importance()

Returns

Named numeric().

---

Method selected_features()

Selected features are extracted from the model slot var.used.

Usage

LearnerRegrRandomForestSRC$selected_features()

Returns

character().

---

Method oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerRegrRandomForestSRC$oob_error()

Returns

numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForestSRC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.rfsrc")
print(learner)
#> <LearnerRegrRandomForestSRC:regr.rfsrc>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, randomForestSRC
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor
#> * Properties: importance, missings, oob_error, weights

# available parameters:
learner$param_set$ids()
#>  [1] "ntree"          "mtry"           "mtry.ratio"     "nodesize"      
#>  [5] "nodedepth"      "splitrule"      "nsplit"         "importance"    
#>  [9] "block.size"     "bootstrap"      "samptype"       "samp"          
#> [13] "membership"     "sampsize"       "sampsize.ratio" "na.action"     
#> [17] "nimpute"        "ntime"          "cause"          "proximity"     
#> [21] "distance"       "forest.wt"      "xvar.wt"        "split.wt"      
#> [25] "forest"         "var.used"       "split.depth"    "seed"          
#> [29] "do.trace"       "statistics"     "get.tree"       "outcome"       
#> [33] "ptn.count"      "cores"          "save.memory"