Featureless Classification LearnerSource:
A simple LearnerClassif which only analyzes 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. Probabilities correspond to the relative frequency of the class labels in the training set.
Randomly predict a label uniformly. Probabilities correspond to a uniform distribution of class labels, i.e. 1 divided by the number of classes.
Randomly predict a label, with probability estimated from the training distribution. For consistency, probabilities are 1 for the sampled label and 0 for all other labels.
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”
Required Packages: mlr3
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:
All features have a score of
0 for this learner.
Selected features are always the empty set for this learner.