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: -
|method||character||mode||\((-\infty, \infty)\)||mode , sample , weighted.sample|
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.