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/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.

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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForest$new()

---

Method 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().

---

Method oob_error()

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

Usage

LearnerClassifRandomForest$oob_error()

Returns

numeric(1).

---

Method 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'

# 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$model)
#> 
#> Call:
#>  randomForest(formula = formula, data = data, classwt = classwt,      cutoff = cutoff, importance = TRUE) 
#>                Type of random forest: classification
#>                      Number of trees: 500
#> No. of variables tried at each split: 7
#> 
#>         OOB estimate of  error rate: 17.27%
#> Confusion matrix:
#>    M  R class.error
#> M 63 10   0.1369863
#> R 14 52   0.2121212
print(learner$importance())
#>           V11           V12            V9           V49           V10 
#>  2.512616e-02  1.434179e-02  1.414167e-02  1.094350e-02  9.763137e-03 
#>           V45           V52           V27            V4           V17 
#>  8.296655e-03  6.452860e-03  6.015117e-03  5.765118e-03  5.667177e-03 
#>           V36           V16           V18           V37           V20 
#>  5.120603e-03  4.794913e-03  4.486377e-03  4.300409e-03  4.232710e-03 
#>           V28           V23           V48           V13           V46 
#>  3.929147e-03  3.774378e-03  3.634302e-03  3.379336e-03  3.355991e-03 
#>           V43           V44           V21           V39           V15 
#>  3.269688e-03  3.128039e-03  3.050413e-03  2.908765e-03  2.893617e-03 
#>            V6            V1           V14           V47            V5 
#>  2.871987e-03  2.656596e-03  2.514821e-03  2.367208e-03  2.217046e-03 
#>           V34           V29           V42           V26           V31 
#>  2.150460e-03  2.077685e-03  1.974702e-03  1.819090e-03  1.706076e-03 
#>            V2           V38           V30           V40           V22 
#>  1.618962e-03  1.515210e-03  1.501629e-03  1.281805e-03  1.148984e-03 
#>           V24           V19           V32            V7           V53 
#>  1.112308e-03  1.098842e-03  1.065101e-03  9.479426e-04  9.166615e-04 
#>           V35           V59           V58           V41            V8 
#>  8.228153e-04  7.804579e-04  7.127746e-04  7.109345e-04  6.977194e-04 
#>           V50           V25            V3           V55           V54 
#>  6.586087e-04  6.522327e-04  5.318932e-04  4.580952e-04  2.382645e-04 
#>           V60           V33           V57           V51           V56 
#>  1.596353e-04  1.050457e-04 -1.476661e-05 -1.227495e-04 -1.452923e-04 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1304348