mlr_learners_classif.OneR | R Documentation |
One Rule classification algorithm that yields an extremely simple model.
Calls RWeka::OneR()
from RWeka.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("classif.OneR") lrn("classif.OneR")
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
B | integer | 6 | [1, \infty) |
|
output_debug_info | logical | FALSE | TRUE, FALSE | - |
do_not_check_capabilities | logical | FALSE | TRUE, FALSE | - |
num_decimal_places | integer | 2 | [1, \infty) |
|
batch_size | integer | 100 | [1, \infty) |
|
options | untyped | NULL | - | |
output_debug_info
:
original id: output-debug-info
do_not_check_capabilities
:
original id: do-not-check-capabilities
num_decimal_places
:
original id: num-decimal-places
batch_size
:
original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifOneR
new()
Creates a new instance of this R6 class.
LearnerClassifOneR$new()
clone()
The objects of this class are cloneable with this method.
LearnerClassifOneR$clone(deep = FALSE)
deep
Whether to make a deep clone.
henrifnk
Holte, C R (1993). “Very simple classification rules perform well on most commonly used datasets.” Machine learning, 11(1), 63–90.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("classif.OneR")
print(learner)
# available parameters:
learner$param_set$ids()
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