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$model)
#> 
#> Call:
#>  randomForest(formula = formula, data = data, classwt = classwt,      cutoff = cutoff, importance = TRUE) 
#>                Type of random forest: classification
#>                      Number of trees: 500
#> No. of variables tried at each split: 7
#> 
#>         OOB estimate of  error rate: 17.99%
#> Confusion matrix:
#>    M  R class.error
#> M 69  9   0.1153846
#> R 16 45   0.2622951
print(learner$importance())
#>           V11           V12           V48           V49            V9 
#>  2.576197e-02  2.430272e-02  1.462756e-02  1.312830e-02  1.295622e-02 
#>           V47           V36           V10           V45           V34 
#>  1.020829e-02  8.552807e-03  7.634520e-03  6.177693e-03  5.772252e-03 
#>           V37           V35           V46           V28           V13 
#>  5.704017e-03  5.434261e-03  5.427647e-03  4.640727e-03  4.319019e-03 
#>           V51            V1           V32           V27            V8 
#>  4.011734e-03  3.923083e-03  3.827175e-03  3.543558e-03  3.320258e-03 
#>           V52            V5           V31           V40           V15 
#>  3.320074e-03  3.214832e-03  3.157960e-03  2.695443e-03  2.609873e-03 
#>           V33            V6           V22            V7           V39 
#>  2.390807e-03  2.204334e-03  1.787588e-03  1.742648e-03  1.717655e-03 
#>           V18           V44            V2           V17           V19 
#>  1.699738e-03  1.644217e-03  1.613029e-03  1.531451e-03  1.488964e-03 
#>           V16           V20            V4           V30           V38 
#>  1.413464e-03  1.324215e-03  1.267769e-03  9.684161e-04  9.381802e-04 
#>           V57            V3           V54           V21           V50 
#>  9.091115e-04  8.889045e-04  8.655015e-04  8.647348e-04  8.117383e-04 
#>           V41           V43           V58           V23           V29 
#>  6.678118e-04  6.358314e-04  5.702619e-04  4.116908e-04  3.508766e-04 
#>           V59           V42           V55           V24           V53 
#>  3.250561e-04  2.915233e-04  2.443078e-04  2.276577e-04  1.663030e-04 
#>           V60           V25           V26           V14           V56 
#>  2.815072e-05 -6.244058e-05 -1.679664e-04 -2.199228e-04 -6.128587e-04 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696