A simple LearnerRegr which only analyses the response during train, ignoring all features.
FALSE (default), constantly predicts
mean(y) as response
sd(y) as standard error.
mad() are used instead of
Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: mlr3, 'stats'
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:
Creates a new instance of this R6 class.
All features have a score of
0 for this learner.
Selected features are always the empty set for this learner.
The objects of this class are cloneable with this method.
LearnerRegrFeatureless$clone(deep = FALSE)
Whether to make a deep clone.