A simple LearnerClassif which only analyses the labels during train, ignoring all features. Hyperparameter method determines the mode of operation during prediction:

mode:

Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.

sample:

Randomly predict a label uniformly.

weighed.sample:

Randomly predict a label, with probability estimated from the training distribution.

Dictionary

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")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

  • Required Packages: -

Parameters

IdTypeDefaultRangeLevels
methodcharactermode\((-\infty, \infty)\)mode , sample , weighted.sample

See also

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifFeatureless

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifFeatureless$new()


Method importance()

All features have a score of 0 for this learner.

Usage

LearnerClassifFeatureless$importance()

Returns

Named numeric().


Method selected_features()

Selected features are always the empty set for this learner.

Usage

LearnerClassifFeatureless$selected_features()

Returns

character(0).


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifFeatureless$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.