Regression Cubist Learner
mlr_learners_regr.cubist.Rd
Rule-based model that is an extension of Quinlan's M5 model tree. Each tree contains
linear regression models at the terminal leaves.
Calls Cubist::cubist()
from Cubist.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.cubist")
mlr_learnerslrn("regr.cubist")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, Cubist
Parameters
Id | Type | Default | Levels | Range |
committees | integer | 1 | \([1, 100]\) | |
unbiased | logical | FALSE | TRUE, FALSE | - |
rules | integer | 100 | \([1, \infty)\) | |
extrapolation | numeric | 100 | \([0, 100]\) | |
sample | integer | 0 | \([0, \infty)\) | |
seed | integer | - | \((-\infty, \infty)\) | |
label | untyped | outcome | - | |
neighbors | integer | 0 | \([0, 9]\) |
References
Quinlan, R J, others (1992). “Learning with continuous classes.” In 5th Australian joint conference on artificial intelligence, volume 92, 343--348. World Scientific.
Quinlan, Ross J (1993). “Combining instance-based and model-based learning.” In Proceedings of the tenth international conference on machine learning, 236--243.
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
-> LearnerRegrCubist
Examples
learner = mlr3::lrn("regr.cubist")
print(learner)
#> <LearnerRegrCubist:regr.cubist>: Rule-based model
#> * Model: -
#> * Parameters: committees=1, neighbors=0
#> * Packages: mlr3, mlr3extralearners, Cubist
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, character, factor, ordered
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "committees" "unbiased" "rules" "extrapolation"
#> [5] "sample" "seed" "label" "neighbors"