mlr_learners_regr.random_forest_weka: Regression Random Forest Learner from Weka

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression Random Forest Learner from Weka

Description

Class for constructing a forest of random trees. 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("regr.random_forest_weka")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• 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		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForestWeka

Methods

Public methods

• LearnerRegrRandomForestWeka$new()

• LearnerRegrRandomForestWeka$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

LearnerRegrRandomForestWeka$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForestWeka$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

damirpolat

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, \Sexpr[results=rd]{tools:::Rd_expr_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.

Examples

# Define the Learner
learner = mlr3::lrn("regr.random_forest_weka")
print(learner)

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

# 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)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.