Regression Random Forest Learner
mlr_learners_regr.randomForest.Rd
Random forest for regression.
Calls randomForest::randomForest()
from randomForest.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.randomForest")
mlr_learnerslrn("regr.randomForest")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, randomForest
Parameters
Id | Type | Default | Levels | Range |
ntree | integer | 500 | \([1, \infty)\) | |
mtry | integer | - | \([1, \infty)\) | |
replace | logical | TRUE | TRUE, FALSE | - |
strata | untyped | - | - | |
sampsize | untyped | - | - | |
nodesize | integer | 5 | \([1, \infty)\) | |
maxnodes | integer | - | \([1, \infty)\) | |
importance | character | FALSE | mse, nudepurity, none | - |
localImp | logical | FALSE | TRUE, FALSE | - |
proximity | logical | FALSE | TRUE, FALSE | - |
oob.prox | logical | - | TRUE, FALSE | - |
norm.votes | logical | TRUE | TRUE, FALSE | - |
do.trace | logical | FALSE | TRUE, FALSE | - |
keep.forest | logical | TRUE | TRUE, FALSE | - |
keep.inbag | logical | FALSE | TRUE, FALSE | - |
predict.all | logical | FALSE | TRUE, FALSE | - |
nodes | logical | FALSE | TRUE, FALSE | - |
References
Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5--32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .
See also
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrRandomForest
Methods
Method importance()
The importance scores are extracted from the slot importance
.
Parameter 'importance' must be set to either "mse"
or "nodepurity"
.
Returns
Named numeric()
.
Examples
learner = mlr3::lrn("regr.randomForest")
print(learner)
#> <LearnerRegrRandomForest:regr.randomForest>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, randomForest
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, oob_error, weights
# available parameters:
learner$param_set$ids()
#> [1] "ntree" "mtry" "replace" "strata" "sampsize"
#> [6] "nodesize" "maxnodes" "importance" "localImp" "proximity"
#> [11] "oob.prox" "norm.votes" "do.trace" "keep.forest" "keep.inbag"
#> [16] "predict.all" "nodes"