Classification 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("classif.random_tree")

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	-		-
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::LearnerClassif -> LearnerClassifRandomTree

Active bindings

marshaled

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

Methods

Public methods

• LearnerClassifRandomTree$new()

• LearnerClassifRandomTree$marshal()

• LearnerClassifRandomTree$unmarshal()

• LearnerClassifRandomTree$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

LearnerClassifRandomTree$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifRandomTree$marshal(...)

Arguments

...

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

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifRandomTree$unmarshal(...)

Arguments

...

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# 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)
#> 
#> RandomTree
#> ==========
#> 
#> V9 < 0.11
#> |   V32 < 0.46
#> |   |   V38 < 0.34
#> |   |   |   V24 < 0.9 : R (12/0)
#> |   |   |   V24 >= 0.9
#> |   |   |   |   V12 < 0.16 : R (1/0)
#> |   |   |   |   V12 >= 0.16 : M (1/0)
#> |   |   V38 >= 0.34
#> |   |   |   V33 < 0.55 : M (7/0)
#> |   |   |   V33 >= 0.55 : R (1/0)
#> |   V32 >= 0.46 : R (17/0)
#> V9 >= 0.11
#> |   V11 < 0.09 : R (5/0)
#> |   V11 >= 0.09
#> |   |   V6 < 0.21
#> |   |   |   V13 < 0.1 : R (3/0)
#> |   |   |   V13 >= 0.1
#> |   |   |   |   V28 < 0.91
#> |   |   |   |   |   V20 < 0.51
#> |   |   |   |   |   |   V25 < 0.9
#> |   |   |   |   |   |   |   V4 < 0.02 : R (5/0)
#> |   |   |   |   |   |   |   V4 >= 0.02
#> |   |   |   |   |   |   |   |   V18 < 0.48
#> |   |   |   |   |   |   |   |   |   V46 < 0.05 : R (2/0)
#> |   |   |   |   |   |   |   |   |   V46 >= 0.05
#> |   |   |   |   |   |   |   |   |   |   V3 < 0.03
#> |   |   |   |   |   |   |   |   |   |   |   V2 < 0.02 : R (1/0)
#> |   |   |   |   |   |   |   |   |   |   |   V2 >= 0.02 : M (1/0)
#> |   |   |   |   |   |   |   |   |   |   V3 >= 0.03 : M (7/0)
#> |   |   |   |   |   |   |   |   V18 >= 0.48 : R (5/0)
#> |   |   |   |   |   |   V25 >= 0.9 : M (4/0)
#> |   |   |   |   |   V20 >= 0.51
#> |   |   |   |   |   |   V51 < 0.01
#> |   |   |   |   |   |   |   V26 < 0.63 : M (5/0)
#> |   |   |   |   |   |   |   V26 >= 0.63
#> |   |   |   |   |   |   |   |   V30 < 0.44
#> |   |   |   |   |   |   |   |   |   V26 < 0.83
#> |   |   |   |   |   |   |   |   |   |   V12 < 0.38 : R (2/0)
#> |   |   |   |   |   |   |   |   |   |   V12 >= 0.38 : M (1/0)
#> |   |   |   |   |   |   |   |   |   V26 >= 0.83 : M (3/0)
#> |   |   |   |   |   |   |   |   V30 >= 0.44 : R (3/0)
#> |   |   |   |   |   |   V51 >= 0.01 : M (22/0)
#> |   |   |   |   V28 >= 0.91
#> |   |   |   |   |   V51 < 0 : R (1/0)
#> |   |   |   |   |   V51 >= 0 : M (22/0)
#> |   |   V6 >= 0.21
#> |   |   |   V31 < 0.15 : M (1/0)
#> |   |   |   V31 >= 0.15 : R (7/0)
#> 
#> Size of the tree : 49


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696