Featureless Regression LearnerSource:
A simple LearnerRegr which only analyzes 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
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function
Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: mlr3, 'stats'
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.
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.