Regression Mars Learner
mlr_learners_regr.mars.Rd
Multivariate Adaptive Regression Splines.
Calls mda::mars()
from mda.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.mars")
mlr_learnerslrn("regr.mars")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, mda
Parameters
Id | Type | Default | Levels | Range |
degree | integer | 1 | \([1, \infty)\) | |
nk | integer | - | \([1, \infty)\) | |
penalty | numeric | 2 | \([0, \infty)\) | |
thresh | numeric | 0.001 | \([0, \infty)\) | |
prune | logical | TRUE | TRUE, FALSE | - |
trace.mars | logical | FALSE | TRUE, FALSE | - |
forward.step | logical | FALSE | TRUE, FALSE | - |
References
Friedman, H J (1991). “Multivariate adaptive regression splines.” The annals of statistics, 19(1), 1--67.
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
-> LearnerRegrMars
Examples
learner = mlr3::lrn("regr.mars")
print(learner)
#> <LearnerRegrMars:regr.mars>: Multivariate Adaptive Regression Splines
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, mda
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "degree" "nk" "penalty" "thresh" "prune"
#> [6] "trace.mars" "forward.step"