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

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.PART")
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\([1, \infty)\)
optionsuntyped-

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

learner = mlr3::lrn("classif.PART")
print(learner)
#> <LearnerClassifPART:classif.PART>: Tree-based Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass

# available parameters:
learner$param_set$ids()
#>  [1] "subset"                         "na.action"                     
#>  [3] "C"                              "M"                             
#>  [5] "R"                              "N"                             
#>  [7] "B"                              "U"                             
#>  [9] "J"                              "Q"                             
#> [11] "doNotMakeSplitPointActualValue" "output_debug_info"             
#> [13] "do_not_check_capabilities"      "num_decimal_places"            
#> [15] "batch_size"                     "options"