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

Methods

Public methods

• LearnerClassifRandomTree$new()

• 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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomTree$new()

---

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 = mlr3::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: missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

# 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)
#> 
#> RandomTree
#> ==========
#> 
#> V10 < 0.15
#> |   V22 < 0.62 : R (24/0)
#> |   V22 >= 0.62
#> |   |   V16 < 0.27
#> |   |   |   V53 < 0.01
#> |   |   |   |   V33 < 0.49 : M (2/0)
#> |   |   |   |   V33 >= 0.49 : R (2/0)
#> |   |   |   V53 >= 0.01 : R (11/0)
#> |   |   V16 >= 0.27
#> |   |   |   V35 < 0.28
#> |   |   |   |   V36 < 0.11 : R (4/0)
#> |   |   |   |   V36 >= 0.11
#> |   |   |   |   |   V24 < 0.79 : R (1/0)
#> |   |   |   |   |   V24 >= 0.79 : M (2/0)
#> |   |   |   V35 >= 0.28 : M (8/0)
#> V10 >= 0.15
#> |   V35 < 0.26
#> |   |   V26 < 0.63
#> |   |   |   V40 < 0.19 : M (4/0)
#> |   |   |   V40 >= 0.19
#> |   |   |   |   V23 < 0.81 : R (4/0)
#> |   |   |   |   V23 >= 0.81 : M (1/0)
#> |   |   V26 >= 0.63
#> |   |   |   V17 < 0.86 : M (34/0)
#> |   |   |   V17 >= 0.86 : R (1/0)
#> |   V35 >= 0.26
#> |   |   V23 < 0.15 : M (4/0)
#> |   |   V23 >= 0.15
#> |   |   |   V54 < 0.02
#> |   |   |   |   V39 < 0.18 : R (9/0)
#> |   |   |   |   V39 >= 0.18
#> |   |   |   |   |   V21 < 0.1 : M (2/0)
#> |   |   |   |   |   V21 >= 0.1
#> |   |   |   |   |   |   V10 < 0.28
#> |   |   |   |   |   |   |   V32 < 0.49
#> |   |   |   |   |   |   |   |   V29 < 0.65 : M (6/0)
#> |   |   |   |   |   |   |   |   V29 >= 0.65 : R (1/0)
#> |   |   |   |   |   |   |   V32 >= 0.49
#> |   |   |   |   |   |   |   |   V57 < 0 : M (1/0)
#> |   |   |   |   |   |   |   |   V57 >= 0 : R (5/0)
#> |   |   |   |   |   |   V10 >= 0.28 : R (7/0)
#> |   |   |   V54 >= 0.02 : M (6/0)
#> 
#> Size of the tree : 43


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3913043