Classification Random Forest Learner
Source:R/learner_randomForest_classif_randomForest.R
mlr_learners_classif.randomForest.Rd
Random forest for classification.
Calls randomForest::randomForest()
from randomForest.
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
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 | - |
classwt | untyped | NULL | - | |
cutoff | untyped | - | - | |
strata | untyped | - | - | |
sampsize | untyped | - | - | |
nodesize | integer | 1 | \([1, \infty)\) | |
maxnodes | integer | - | \([1, \infty)\) | |
importance | character | FALSE | accuracy, gini, 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::LearnerClassif
-> LearnerClassifRandomForest
Methods
Inherited methods
mlr3::Learner$base_learner()
mlr3::Learner$configure()
mlr3::Learner$encapsulate()
mlr3::Learner$format()
mlr3::Learner$help()
mlr3::Learner$predict()
mlr3::Learner$predict_newdata()
mlr3::Learner$print()
mlr3::Learner$reset()
mlr3::Learner$selected_features()
mlr3::Learner$train()
mlr3::LearnerClassif$predict_newdata_fast()
Method importance()
The importance scores are extracted from the slot importance
.
Parameter 'importance' must be set to either "accuracy"
or "gini"
.
Returns
Named numeric()
.
Examples
# Define the Learner
learner = lrn("classif.randomForest", importance = "accuracy")
print(learner)
#>
#> ── <LearnerClassifRandomForest> (classif.randomForest): Random Forest ──────────
#> • Model: -
#> • Parameters: importance=accuracy
#> • Packages: mlr3, mlr3extralearners, and randomForest
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, multiclass, oob_error, twoclass, and weights
#> • Other settings: use_weights = 'use'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#>
#> Call:
#> randomForest(formula = formula, data = data, classwt = classwt, cutoff = cutoff, importance = TRUE)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 7
#>
#> OOB estimate of error rate: 19.42%
#> Confusion matrix:
#> M R class.error
#> M 66 7 0.09589041
#> R 20 46 0.30303030
print(learner$importance())
#> V11 V46 V9 V49 V12
#> 2.149306e-02 1.303853e-02 1.270259e-02 1.257787e-02 1.239478e-02
#> V48 V10 V20 V13 V4
#> 9.594104e-03 9.350289e-03 9.169662e-03 8.942942e-03 8.054126e-03
#> V45 V47 V16 V21 V18
#> 8.001914e-03 7.636911e-03 5.331896e-03 4.397556e-03 4.094982e-03
#> V27 V44 V51 V31 V19
#> 3.864285e-03 3.683499e-03 3.593009e-03 3.224122e-03 3.021729e-03
#> V42 V37 V50 V54 V15
#> 2.968945e-03 2.954751e-03 2.933879e-03 2.877436e-03 2.677633e-03
#> V29 V28 V52 V22 V43
#> 2.627352e-03 2.617658e-03 2.257927e-03 1.928297e-03 1.924005e-03
#> V36 V30 V1 V32 V38
#> 1.911702e-03 1.874922e-03 1.696440e-03 1.689376e-03 1.655204e-03
#> V41 V17 V7 V26 V14
#> 1.613820e-03 1.602538e-03 1.560251e-03 1.533503e-03 1.510058e-03
#> V59 V6 V58 V23 V39
#> 1.484464e-03 1.437414e-03 1.383361e-03 1.373362e-03 1.355256e-03
#> V35 V8 V53 V55 V5
#> 1.229607e-03 1.190785e-03 1.043537e-03 1.031794e-03 9.278910e-04
#> V2 V3 V60 V33 V56
#> 6.738709e-04 4.388991e-04 3.675236e-04 3.360776e-04 2.710605e-04
#> V25 V57 V40 V34 V24
#> 2.461356e-04 1.486063e-04 -7.145667e-05 -4.040398e-04 -6.019170e-04
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.1594203