mlr_learners_classif.featureless function

Featureless Classification Learner

A simple LearnerClassif which only analyzes 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. Probabilities correspond to the relative frequency of the class labels in the training set.
• sample:: Randomly predict a label uniformly. Probabilities correspond to a uniform distribution of class labels, i.e. 1 divided by the number of classes.
• weighted.sample:: 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.

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 , POSIXct , Date
• Required Packages: list("mlr3")

Parameters

Id	Type	Default	Levels
method	character	mode	mode, sample, weighted.sample

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package list("mlr3learners") for a solid collection of essential learners.

• Package mlr3extralearners for more learners.

• Dictionary of Learners : mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• list("mlr3pipelines") to combine learners with pre- and postprocessing steps.

• Package list("mlr3viz") for some generic visualizations.

• Extension packages for additional task types:

  • list("mlr3proba") for probabilistic supervised regression and survival analysis.
  • list("mlr3cluster") for unsupervised clustering.

• list("mlr3tuning") for tuning of hyperparameters, list("mlr3tuningspaces")

  for established default tuning spaces.

Other Learner: Learner, LearnerClassif, LearnerRegr, mlr_learners, mlr_learners_classif.debug, mlr_learners_classif.rpart, mlr_learners_regr.debug, mlr_learners_regr.featureless, mlr_learners_regr.rpart

Super classes

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

Methods

Public methods

• list("LearnerClassifFeatureless$new()")

• list("LearnerClassifFeatureless$importance()")

• list("LearnerClassifFeatureless$selected_features()")

• list("LearnerClassifFeatureless$clone()")

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.