mlr3 (version 0.5.0)

mlr_learners_classif.featureless: Featureless Classification Learner

Description

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

Id	Type	Default	Range	Levels
method	character	mode	\((-\infty, \infty)\)	mode , sample , weighted.sample

Super classes

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

Methods

Public methods

• LearnerClassifFeatureless$new()

• LearnerClassifFeatureless$importance()

• LearnerClassifFeatureless$selected_features()

• LearnerClassifFeatureless$clone()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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.

See Also

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