Polynomial GLM.
Calls polyreg::polyFit()
from polyreg.
Initial parameter values
deg
: We have set this to 2, pretty arbitrarily.noisy
: We have set this to FALSE, to get no output on the console.
Meta Information
Task type: “classif”
Predict Types: “response”
Feature Types: “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, polyreg
Parameters
Id | Type | Default | Levels | Range |
deg | integer | - | \([0, \infty)\) | |
maxInteractDeg | integer | - | \([0, \infty)\) | |
return_xy | logical | FALSE | TRUE, FALSE | - |
returnPoly | logical | FALSE | TRUE, FALSE | - |
noisy | logical | TRUE | TRUE, FALSE | - |
glmMethod | character | one | one, all | - |
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
-> LearnerClassifPolyFit
Methods
Inherited methods
mlr3::Learner$base_learner()
mlr3::Learner$configure()
mlr3::Learner$encapsulate()
mlr3::Learner$format()
mlr3::Learner$help()
mlr3::Learner$predict()
mlr3::Learner$predict_newdata()
mlr3::Learner$print()
mlr3::Learner$reset()
mlr3::Learner$selected_features()
mlr3::Learner$train()
mlr3::LearnerClassif$predict_newdata_fast()