Regression Random Forest SRC Learner
mlr_learners_regr.rfsrc.Rd
Random forest for regression.
Calls randomForestSRC::rfsrc()
from randomForestSRC.
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 | 500 | \([1, \infty)\) | |
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 | - |
perf.type | character | - | none | - |
case.depth | logical | FALSE | TRUE, FALSE | - |
Custom mlr3 parameters
mtry
: This hyperparameter can alternatively be set via the added hyperparametermtry.ratio
asmtry = max(ceiling(mtry.ratio * n_features), 1)
. Note thatmtry
andmtry.ratio
are mutually exclusive.sampsize
: This hyperparameter can alternatively be set via the added hyperparametersampsize.ratio
assampsize = max(ceiling(sampsize.ratio * n_obs), 1)
. Note thatsampsize
andsampsize.ratio
are mutually exclusive.cores
: This value is set as the optionrf.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
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
-> LearnerRegrRandomForestSRC
Methods
Method importance()
The importance scores are extracted from the model slot importance
.
Returns
Named numeric()
.
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