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
#> ==========
#> 
#> V21 < 0.63
#> |   V52 < 0.01
#> |   |   V25 < 0.96 : R (21/0)
#> |   |   V25 >= 0.96 : M (1/0)
#> |   V52 >= 0.01
#> |   |   V12 < 0.22
#> |   |   |   V48 < 0.12 : R (17/0)
#> |   |   |   V48 >= 0.12
#> |   |   |   |   V44 < 0.25 : R (4/0)
#> |   |   |   |   V44 >= 0.25
#> |   |   |   |   |   V8 < 0.16 : M (5/0)
#> |   |   |   |   |   V8 >= 0.16 : R (1/0)
#> |   |   V12 >= 0.22
#> |   |   |   V17 < 0.51
#> |   |   |   |   V15 < 0.37
#> |   |   |   |   |   V27 < 0.5
#> |   |   |   |   |   |   V17 < 0.29 : R (1/0)
#> |   |   |   |   |   |   V17 >= 0.29 : M (1/0)
#> |   |   |   |   |   V27 >= 0.5 : M (18/0)
#> |   |   |   |   V15 >= 0.37 : R (2/0)
#> |   |   |   V17 >= 0.51
#> |   |   |   |   V54 < 0.01 : R (3/0)
#> |   |   |   |   V54 >= 0.01 : M (1/0)
#> V21 >= 0.63
#> |   V28 < 0.91
#> |   |   V9 < 0.16
#> |   |   |   V4 < 0.06
#> |   |   |   |   V22 < 0.8
#> |   |   |   |   |   V10 < 0.11 : R (1/0)
#> |   |   |   |   |   V10 >= 0.11
#> |   |   |   |   |   |   V10 < 0.21 : M (2/0)
#> |   |   |   |   |   |   V10 >= 0.21 : R (1/0)
#> |   |   |   |   V22 >= 0.8 : R (7/0)
#> |   |   |   V4 >= 0.06
#> |   |   |   |   V34 < 0.6
#> |   |   |   |   |   V38 < 0.18 : R (1/0)
#> |   |   |   |   |   V38 >= 0.18 : M (8/0)
#> |   |   |   |   V34 >= 0.6 : R (1/0)
#> |   |   V9 >= 0.16
#> |   |   |   V5 < 0.16
#> |   |   |   |   V52 < 0 : R (1/0)
#> |   |   |   |   V52 >= 0
#> |   |   |   |   |   V50 < 0.01
#> |   |   |   |   |   |   V22 < 0.34 : R (1/0)
#> |   |   |   |   |   |   V22 >= 0.34 : M (3/0)
#> |   |   |   |   |   V50 >= 0.01 : M (21/0)
#> |   |   |   V5 >= 0.16
#> |   |   |   |   V13 < 0.58 : R (3/0)
#> |   |   |   |   V13 >= 0.58 : M (1/0)
#> |   V28 >= 0.91 : M (13/0)
#> 
#> Size of the tree : 51


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2463768