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$model)
#>
#> Call:
#> randomForest(formula = formula, data = data, classwt = classwt, cutoff = cutoff, importance = TRUE)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 7
#>
#> OOB estimate of error rate: 17.99%
#> Confusion matrix:
#> M R class.error
#> M 65 8 0.1095890
#> R 17 49 0.2575758
print(learner$importance())
#> V11 V10 V9 V12 V36
#> 3.087154e-02 2.177442e-02 1.756430e-02 1.646963e-02 1.442908e-02
#> V13 V52 V47 V48 V37
#> 1.380753e-02 9.353004e-03 5.887479e-03 5.390828e-03 5.045437e-03
#> V21 V45 V3 V51 V27
#> 4.303841e-03 4.223506e-03 4.104018e-03 4.103929e-03 3.621218e-03
#> V24 V20 V28 V4 V54
#> 3.558595e-03 3.532400e-03 3.465018e-03 3.421179e-03 3.383128e-03
#> V46 V17 V29 V49 V5
#> 3.259465e-03 3.150783e-03 2.783448e-03 2.770323e-03 2.500925e-03
#> V59 V22 V32 V18 V35
#> 2.431304e-03 2.291084e-03 2.061683e-03 2.027130e-03 2.020046e-03
#> V16 V23 V38 V1 V8
#> 1.994473e-03 1.964263e-03 1.717433e-03 1.716608e-03 1.708446e-03
#> V25 V15 V30 V53 V26
#> 1.575099e-03 1.479429e-03 1.464152e-03 1.456090e-03 1.257613e-03
#> V34 V42 V40 V19 V33
#> 1.081468e-03 1.068493e-03 1.010806e-03 9.858622e-04 9.222749e-04
#> V31 V44 V2 V58 V14
#> 8.741324e-04 8.307984e-04 7.711061e-04 7.513344e-04 6.619787e-04
#> V6 V50 V57 V7 V43
#> 6.306368e-04 4.497210e-04 4.425969e-04 4.052465e-04 2.963889e-04
#> V55 V60 V56 V39 V41
#> 1.570490e-04 3.932018e-05 -7.473877e-05 -4.217541e-04 -6.110070e-04
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.173913