A simple LearnerRegr which only analyzes the response during train, ignoring all features.
If hyperparameter robust is FALSE (default), constantly predicts mean(y) as response
and sd(y) as standard error.
If robust is TRUE, median() and mad() are used instead of mean() and sd(),
respectively.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
mlr_learners$get("regr.featureless")
lrn("regr.featureless")Meta Information
Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: mlr3, 'stats'
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
Package mlr3learners for a solid collection of essential learners.
Package mlr3extralearners for more learners.
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Package mlr3viz for some generic visualizations.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
LearnerClassif,
LearnerRegr,
Learner,
mlr_learners_classif.debug,
mlr_learners_classif.featureless,
mlr_learners_classif.rpart,
mlr_learners_regr.debug,
mlr_learners_regr.rpart,
mlr_learners
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFeatureless