Classification Random Forest Learner

Random forest for classification. Calls randomForest::randomForest() from randomForest.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.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

• Dictionary of Learners: mlr3::mlr_learners.

• 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/chapters/chapter2/data_and_basic_modeling.html#sec-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.

Author

pat-s

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForest

Methods

Public methods

• LearnerClassifRandomForest$new()

• LearnerClassifRandomForest$importance()

• LearnerClassifRandomForest$oob_error()

• LearnerClassifRandomForest$clone()

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()

---

LearnerClassifRandomForest$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForest$new()

---

LearnerClassifRandomForest$importance()

The importance scores are extracted from the slot importance. Parameter 'importance' must be set to either "accuracy" or "gini".

Usage

LearnerClassifRandomForest$importance()

Returns

Named numeric().

---

LearnerClassifRandomForest$oob_error()

OOB errors are extracted from the model slot err.rate.

Usage

LearnerClassifRandomForest$oob_error()

Returns

numeric(1).

---

LearnerClassifRandomForest$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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', predict_raw = 'FALSE'

# 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$model)
#> 
#> Call:
#>  randomForest(formula = formula, data = data, classwt = classwt,      cutoff = cutoff, importance = TRUE) 
#>                Type of random forest: classification
#>                      Number of trees: 500
#> No. of variables tried at each split: 7
#> 
#>         OOB estimate of  error rate: 20.14%
#> Confusion matrix:
#>    M  R class.error
#> M 64 10   0.1351351
#> R 18 47   0.2769231
print(learner$importance())
#>           V12           V11           V10            V9           V48 
#>  1.987131e-02  1.906059e-02  1.665038e-02  1.441091e-02  1.434233e-02 
#>           V45           V47           V36           V49           V27 
#>  1.062669e-02  9.854909e-03  9.122597e-03  7.370845e-03  6.144210e-03 
#>           V28           V46           V35            V1            V5 
#>  6.064234e-03  5.736806e-03  5.274450e-03  4.213391e-03  4.200682e-03 
#>           V17           V18           V16           V43           V44 
#>  3.455801e-03  3.452889e-03  3.450880e-03  3.115157e-03  3.030394e-03 
#>           V32           V14           V21           V13           V31 
#>  3.012127e-03  2.879339e-03  2.865058e-03  2.738048e-03  2.660400e-03 
#>           V19           V52           V34           V37            V4 
#>  2.613975e-03  2.491032e-03  2.427418e-03  2.366498e-03  2.031023e-03 
#>           V20            V6           V23           V59           V51 
#>  1.946950e-03  1.875396e-03  1.608503e-03  1.563258e-03  1.355024e-03 
#>           V33            V8           V22           V25           V41 
#>  1.309644e-03  1.246478e-03  1.227057e-03  1.089898e-03  9.880796e-04 
#>           V30           V55           V42           V15           V26 
#>  9.816094e-04  9.240056e-04  8.901118e-04  8.507006e-04  6.721885e-04 
#>            V3           V24           V29           V53            V2 
#>  6.668916e-04  5.893514e-04  5.890289e-04  5.342387e-04  4.558808e-04 
#>           V58           V54           V38           V57           V39 
#>  2.597872e-04  2.411815e-04  1.418241e-04  1.309936e-04  1.209436e-04 
#>           V60           V40           V56           V50            V7 
#>  9.339893e-05  4.520029e-05 -5.898444e-05 -3.077888e-04 -6.846545e-04 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2173913