mlr3 (version 1.0.0)

mlr_learners_regr.featureless: Featureless Regression Learner

Description

A simple LearnerRegr which only analyzes the response during train, ignoring all features. If hyperparameter robust is FALSE (default), constantly predicts mean(y) as response and sd(y) as standard error. If robust is TRUE, median() and mad() are used instead of mean() and sd(), respectively.

For weighted data, the response is the weighted mean (weighted median for robust regression). The predicted standard error is the square root of the weighted variance estimator with bias correction based on effective degrees of freedom:

sd(y, weights) = sqrt(
  sum(weights * (y - weighted.mean(y, weights))^2) /
    (sum(weights) - sum(weights ^2) / sum(weights))
)

If robust is TRUE, the weighted median absolute deviation is used, adjusted by a factor of 1.4826 for consistency with mad().

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.featureless")
lrn("regr.featureless")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”, “quantiles”

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

• Required Packages: mlr3, 'stats'

Parameters

Id	Type	Default	Levels
robust	logical	TRUE	TRUE, FALSE

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFeatureless

Methods

Public methods

• LearnerRegrFeatureless$new()

• LearnerRegrFeatureless$importance()

• LearnerRegrFeatureless$selected_features()

• LearnerRegrFeatureless$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$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

LearnerRegrFeatureless$new()

---

Method importance()

All features have a score of 0 for this learner.

Usage

LearnerRegrFeatureless$importance()

Returns

Named numeric().

---

Method selected_features()

Selected features are always the empty set for this learner.

Usage

LearnerRegrFeatureless$selected_features()

Returns

character(0).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

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