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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

IdTypeDefaultLevelsRange
ntreeinteger1000\([1, \infty)\)
mtryinteger-\([1, \infty)\)
mtry.rationumeric-\([0, 1]\)
nodesizeinteger15\([1, \infty)\)
nodedepthinteger-\([1, \infty)\)
splitrulecharactermsemse, quantile.regr, la.quantile.regr-
nsplitinteger10\([0, \infty)\)
importancecharacterFALSEFALSE, TRUE, none, permute, random, anti-
block.sizeinteger10\([1, \infty)\)
bootstrapcharacterby.rootby.root, by.node, none, by.user-
samptypecharactersworswor, swr-
sampuntyped--
membershiplogicalFALSETRUE, FALSE-
sampsizeuntyped--
sampsize.rationumeric-\([0, 1]\)
na.actioncharacterna.omitna.omit, na.impute-
nimputeinteger1\([1, \infty)\)
ntimeinteger-\([1, \infty)\)
causeinteger-\([1, \infty)\)
proximitycharacterFALSEFALSE, TRUE, inbag, oob, all-
distancecharacterFALSEFALSE, TRUE, inbag, oob, all-
forest.wtcharacterFALSEFALSE, TRUE, inbag, oob, all-
xvar.wtuntyped--
split.wtuntyped--
forestlogicalTRUETRUE, FALSE-
var.usedcharacterFALSEFALSE, all.trees, by.tree-
split.depthcharacterFALSEFALSE, all.trees, by.tree-
seedinteger-\((-\infty, -1]\)
do.tracelogicalFALSETRUE, FALSE-
statisticslogicalFALSETRUE, FALSE-
get.treeuntyped--
outcomecharactertraintrain, test-
ptn.countinteger0\([0, \infty)\)
coresinteger1\([1, \infty)\)
save.memorylogicalFALSETRUE, 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

Author

RaphaelS1

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


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"