Regression M5Rules Learner
mlr_learners_regr.M5Rules.Rd
Algorithm for inducing decision lists from
model trees.
Calls RWeka::M5Rules()
from RWeka.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.M5Rules")
mlr_learnerslrn("regr.M5Rules")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
N | logical | FALSE | TRUE, FALSE | - |
U | logical | FALSE | TRUE, FALSE | - |
R | logical | FALSE | TRUE, FALSE | - |
M | integer | 4 | \((-\infty, \infty)\) | |
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 | - |
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
Holmes, Geoffrey, Hall, Mark, Prank, Eibe (1999). “Generating rule sets from model trees.” In Australasian joint conference on artificial intelligence, 1--12. Springer.
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::LearnerRegr
-> LearnerRegrM5Rules
Examples
learner = mlr3::lrn("regr.M5Rules")
print(learner)
#> <LearnerRegrM5Rules:regr.M5Rules>: Rule-based Algorithm
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "subset" "na.action"
#> [3] "N" "U"
#> [5] "R" "M"
#> [7] "output_debug_info" "do_not_check_capabilities"
#> [9] "num_decimal_places" "batch_size"
#> [11] "options"