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Class for constructing a random forest. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

  • output_debug_info:

    • original id: output-debug-info

  • do_not_check_capabilities:

    • original id: do-not-check-capabilities

  • num_decimal_places:

    • original id: num-decimal-places

  • batch_size:

    • original id: batch-size

  • store_out_of_bag_predictions:

    • original id: store-out-of-bag-predictions

  • output_out_of_bag_complexity_statistics:

    • original id: output-out-of-bag-complexity-statistics

  • num_slots:

    • original id: num-slots

  • Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

  • attribute-importance removed:

    • Compute and output attribute importance (mean impurity decrease method)

  • Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.random_forest_weka")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Pnumeric100\([0, 100]\)
OlogicalFALSETRUE, FALSE-
store_out_of_bag_predictionslogicalFALSETRUE, FALSE-
output_out_of_bag_complexity_statisticslogicalFALSETRUE, FALSE-
printlogicalFALSETRUE, FALSE-
Iinteger100\([1, \infty)\)
num_slotsinteger1\((-\infty, \infty)\)
Kinteger0\((-\infty, \infty)\)
Minteger1\([1, \infty)\)
Vnumeric0.001\((-\infty, \infty)\)
Sinteger1\((-\infty, \infty)\)
depthinteger0\([0, \infty)\)
Ninteger0\((-\infty, \infty)\)
UlogicalFALSETRUE, FALSE-
BlogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .

See also

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForestWeka

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForestWeka$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.random_forest_weka")
print(learner)
#> <LearnerClassifRandomForestWeka:classif.random_forest_weka>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> RandomForest
#> 
#> Bagging with 100 iterations and base learner
#> 
#> weka.classifiers.trees.RandomTree -K 0 -M 1.0 -V 0.001 -S 1 -do-not-check-capabilities


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2028986