mlr_learners_classif.featureless: Featureless Classification Learner

In mlr3: Machine Learning in R - Next Generation

Featureless Classification Learner

Description

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”

• Required Packages: mlr3

Parameters

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

Super classes

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

Methods

Public methods

• LearnerClassifFeatureless$new()

• LearnerClassifFeatureless$importance()

• LearnerClassifFeatureless$selected_features()

• LearnerClassifFeatureless$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifFeatureless$new()

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Method importance()

All features have a score of 0 for this learner.

Usage

LearnerClassifFeatureless$importance()

Returns

Named numeric().

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Method selected_features()

Selected features are always the empty set for this learner.

Usage

LearnerClassifFeatureless$selected_features()

Returns

character(0).

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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

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

• Package 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).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Package mlr3viz for some generic visualizations.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

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

mlr3 documentation built on Nov. 17, 2023, 5:07 p.m.