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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rfsrc")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3, mlr3extralearners, randomForestSRC

Parameters

IdTypeDefaultLevelsRange
ntreeinteger500\([1, \infty)\)
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-
perf.typecharacter-none-
case.depthlogicalFALSETRUE, FALSE-

Custom mlr3 parameters

  • 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.

  • cores: This value is set as the option rf.cores during training and is set to 1 by default.

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

# Define the Learner
learner = mlr3::lrn("regr.rfsrc", importance = "TRUE")
print(learner)
#> <LearnerRegrRandomForestSRC:regr.rfsrc>: Random Forest
#> * Model: -
#> * Parameters: importance=TRUE
#> * Packages: mlr3, mlr3extralearners, randomForestSRC
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: importance, missings, oob_error, weights

# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#>                          Sample size: 21
#>                      Number of trees: 500
#>            Forest terminal node size: 5
#>        Average no. of terminal nodes: 2.416
#> No. of variables tried at each split: 4
#>               Total no. of variables: 10
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 13
#>                             Analysis: RF-R
#>                               Family: regr
#>                       Splitting rule: mse *random*
#>        Number of random split points: 10
#>                      (OOB) R squared: 0.67055107
#>    (OOB) Requested performance error: 11.76250331
#> 
print(learner$importance())
#>       disp         wt        cyl         hp       drat         vs         am 
#> 19.6977247 17.8207051 15.0154474 11.0194863  1.0656363  0.5083748  0.3043696 
#>       gear       qsec       carb 
#>  0.2957660  0.2121084  0.1466123 

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

# Score the predictions
predictions$score()
#> regr.mse 
#> 8.155538