Regression Random Forest SRC Learner

Calls randomForestSRC::rfsrc from package 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")

Traits

• Packages: randomForestSRC

• Predict Types: response

• Feature Types: logical, integer, numeric, factor

• Properties: importance, missings, oob_error, weights

References

Breiman L (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

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$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

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("regr.rfsrc"))
print(learner)
#> <LearnerRegrRandomForestSRC:regr.rfsrc>
#> * Model: -
#> * Parameters: list()
#> * Packages: 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"        "nodesize"    "nodedepth"   "splitrule"  
#>  [6] "nsplit"      "importance"  "block.size"  "ensemble"    "bootstrap"  
#> [11] "samptype"    "samp"        "membership"  "sampsize"    "na.action"  
#> [16] "nimpute"     "ntime"       "cause"       "proximity"   "distance"   
#> [21] "forest.wt"   "xvar.wt"     "split.wt"    "forest"      "var.used"   
#> [26] "split.depth" "seed"        "do.trace"    "statistics"  "get.tree"   
#> [31] "outcome"     "ptn.count"