A simple LearnerClassif which only analyses the labels during train, ignoring all features.
method determines the mode of operation during prediction:
Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.
Randomly predict a label uniformly.
Randomly predict a label, with probability estimated from the training distribution.
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: mlr3
|method||character||mode||-||mode, sample, weighted.sample|
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.
LearnerClassifFeatureless$clone(deep = FALSE)
Whether to make a deep clone.