Featureless Regression Learner
A simple LearnerRegr which only analyses 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.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
mlr_learners$get("regr.featureless")
lrn("regr.featureless")Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: 'stats'
| Id | Type | Default | Range | Levels |
| robust | logical | TRUE | (-Inf, Inf) | TRUE, FALSE |
mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFeatureless
new()
Creates a new instance of this R6 class.
LearnerRegrFeatureless$new()
importance()
All features have a score of 0 for this learner.
LearnerRegrFeatureless$importance()
Named numeric().
selected_features()
Selected features are always the empty set for this learner.
LearnerRegrFeatureless$selected_features()
character(0).
clone()
The objects of this class are cloneable with this method.
LearnerRegrFeatureless$clone(deep = FALSE)
deepWhether to make a deep clone.
as.data.table(mlr_learners) for a complete table of all (also dynamically created) Learner implementations.
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