Classification PART Learner

Regression partition tree. Calls RWeka::PART() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.PART")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
C	numeric	0.25		\([2.22044604925031e-16, 1]\)
M	integer	2		\([1, \infty)\)
R	logical	FALSE	TRUE, FALSE	-
N	integer	3		\([1, \infty)\)
B	logical	FALSE	TRUE, FALSE	-
U	logical	FALSE	TRUE, FALSE	-
J	logical	FALSE	TRUE, FALSE	-
Q	integer	1		\([1, \infty)\)
doNotMakeSplitPointActualValue	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		-

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

References

Frank, Eibe, Witten, H I (1998). “Generating accurate rule sets without global optimization.”

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

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifPART

Methods

Public methods

• LearnerClassifPART$new()

• LearnerClassifPART$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

LearnerClassifPART$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifPART$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.PART")
print(learner)
#> 
#> ── <LearnerClassifPART> (classif.PART): Tree-based Model ───────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: 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)
#> PART decision list
#> ------------------
#> 
#> V12 <= 0.1654 AND
#> V20 <= 0.8321: R (37.0/1.0)
#> 
#> V2 <= 0.0575 AND
#> V39 <= 0.6356 AND
#> V28 > 0.8965: M (23.0)
#> 
#> V2 > 0.0575: M (21.0)
#> 
#> V23 > 0.7839 AND
#> V16 > 0.2764: M (19.0/1.0)
#> 
#> V31 <= 0.8055 AND
#> V59 <= 0.0116 AND
#> V25 <= 0.8337: R (24.0)
#> 
#> V9 > 0.1122: M (10.0)
#> 
#> : R (5.0/1.0)
#> 
#> Number of Rules  : 	7
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2318841