A simple LearnerClassif which only analyses the labels during train, ignoring all features.
Hyperparameter 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.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("classif.featureless") lrn("classif.featureless")
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifFeatureless
new()
Creates a new instance of this R6 class.
LearnerClassifFeatureless$new()
importance()
All features have a score of 0
for this learner.
LearnerClassifFeatureless$importance()
Named numeric()
.
selected_features()
Selected features are always the empty set for this learner.
LearnerClassifFeatureless$selected_features()
character(0)
.
clone()
The objects of this class are cloneable with this method.
LearnerClassifFeatureless$clone(deep = FALSE)
deep
Whether to make a deep clone.
Dictionary of Learners: mlr_learners
as.data.table(mlr_learners)
for a complete table of all (also dynamically created) Learner implementations.
Other Learner:
LearnerClassif
,
LearnerRegr
,
Learner
,
mlr_learners_classif.debug
,
mlr_learners_classif.rpart
,
mlr_learners_regr.featureless
,
mlr_learners_regr.rpart
,
mlr_learners