Regression Random Tree Learner

Tree that considers K randomly chosen attributes at each node. 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

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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.random_tree")

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	-		-
K	integer	0		\([0, \infty)\)
M	integer	1		\([1, \infty)\)
V	numeric	0.001		\((-\infty, \infty)\)
S	integer	1		\((-\infty, \infty)\)
depth	integer	0		\([0, \infty)\)
N	integer	0		\([0, \infty)\)
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		-

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::LearnerRegr -> LearnerRegrRandomTree

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerRegrRandomTree$new()

• LearnerRegrRandomTree$marshal()

• LearnerRegrRandomTree$unmarshal()

• LearnerRegrRandomTree$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$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomTree$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerRegrRandomTree$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrRandomTree$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.random_tree")
print(learner)
#> 
#> ── <LearnerRegrRandomTree> (regr.random_tree): Random Tree ─────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal and missings
#> • Other settings: use_weights = 'error'

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

# 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)
#> 
#> RandomTree
#> ==========
#> 
#> cyl < 5
#> |   wt < 1.89
#> |   |   wt < 1.73 : 30.4 (2/0)
#> |   |   wt >= 1.73 : 33.9 (1/0)
#> |   wt >= 1.89
#> |   |   wt < 2.23
#> |   |   |   disp < 99.65 : 27.3 (1/0)
#> |   |   |   disp >= 99.65 : 26 (1/0)
#> |   |   wt >= 2.23
#> |   |   |   wt < 3.17
#> |   |   |   |   wt < 2.39 : 22.8 (1/0)
#> |   |   |   |   wt >= 2.39
#> |   |   |   |   |   disp < 130.45 : 21.5 (1/0)
#> |   |   |   |   |   disp >= 130.45 : 22.8 (1/0)
#> |   |   |   wt >= 3.17 : 24.4 (1/0)
#> cyl >= 5
#> |   gear < 3.5
#> |   |   hp < 192.5
#> |   |   |   disp < 332
#> |   |   |   |   qsec < 19.11
#> |   |   |   |   |   disp < 289.9
#> |   |   |   |   |   |   qsec < 17.8 : 17.3 (1/0)
#> |   |   |   |   |   |   qsec >= 17.8 : 15.2 (1/0)
#> |   |   |   |   |   disp >= 289.9 : 15.2 (1/0)
#> |   |   |   |   qsec >= 19.11 : 18.1 (1/0)
#> |   |   |   disp >= 332 : 18.7 (1/0)
#> |   |   hp >= 192.5
#> |   |   |   disp < 450 : 14.7 (1/0)
#> |   |   |   disp >= 450 : 10.4 (2/0)
#> |   gear >= 3.5
#> |   |   hp < 116.5 : 21 (1/0)
#> |   |   hp >= 116.5
#> |   |   |   gear < 4.5
#> |   |   |   |   qsec < 18.6 : 19.2 (1/0)
#> |   |   |   |   qsec >= 18.6 : 17.8 (1/0)
#> |   |   |   gear >= 4.5 : 19.7 (1/0)
#> 
#> Size of the tree : 37


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 7.043636