Classification PART Learner
mlr_learners_classif.PART.Rd
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()
:
$get("classif.PART")
mlr_learnerslrn("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 | - |
Initial parameter values
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
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.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifPART
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"