Classification Random Forest Learner from Weka

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

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	numeric	100		\([0, 100]\)
O	logical	FALSE	TRUE, FALSE	-
store_out_of_bag_predictions	logical	FALSE	TRUE, FALSE	-
output_out_of_bag_complexity_statistics	logical	FALSE	TRUE, FALSE	-
print	logical	FALSE	TRUE, FALSE	-
I	integer	100		\([1, \infty)\)
num_slots	integer	1		\((-\infty, \infty)\)
K	integer	0		\((-\infty, \infty)\)
M	integer	1		\([1, \infty)\)
V	numeric	0.001		\((-\infty, \infty)\)
S	integer	1		\((-\infty, \infty)\)
depth	integer	0		\([0, \infty)\)
N	integer	0		\((-\infty, \infty)\)
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
options	untyped	NULL		-

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

damirpolat

Super classes

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

Methods

Public methods

• LearnerClassifRandomForestWeka$new()

• LearnerClassifRandomForestWeka$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestWeka$new()

---

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 
#>  0.1884058