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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

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Cnumeric0.25\([2.22044604925031e-16, 1]\)
Minteger2\([1, \infty)\)
RlogicalFALSETRUE, FALSE-
Ninteger3\([1, \infty)\)
BlogicalFALSETRUE, FALSE-
UlogicalFALSETRUE, FALSE-
JlogicalFALSETRUE, FALSE-
Qinteger1\([1, \infty)\)
doNotMakeSplitPointActualValuelogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

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

Author

henrifnk

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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