Regression Learner for Debugging

A simple LearnerRegr used primarily in the unit tests and for debugging purposes. If no hyperparameter is set, it simply constantly predicts the mean value of the training data. The following hyperparameters trigger the following actions:

predict_missing:

Ratio of predictions which will be NA.

predict_missing_type:

To to encode missingness. “na” will insert NA values, “omit” will just return fewer predictions than requested.

save_tasks:

Saves input task in model slot during training and prediction.

threads:

Number of threads to use. Has no effect.

x:

Numeric tuning parameter. Has no effect.

Dictionary

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

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

Meta Information

• Task type: “regr”

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

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

• Required Packages: mlr3

Parameters

Id	Type	Default	Levels	Range
predict_missing	numeric	0		\([0, 1]\)
predict_missing_type	character	na	na, omit	-
save_tasks	logical	FALSE	TRUE, FALSE	-
threads	integer	-		\([1, \infty)\)
x	numeric	-		\([0, 1]\)

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.featureless, mlr_learners_regr.rpart

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrDebug

Methods

Public methods

• LearnerRegrDebug$new()

• LearnerRegrDebug$importance()

• LearnerRegrDebug$selected_features()

• LearnerRegrDebug$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

LearnerRegrDebug$new()

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Method importance()

Returns 0 for each feature seen in training.

Usage

LearnerRegrDebug$importance()

Returns

Named numeric().

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Method selected_features()

Always returns character(0).

Usage

LearnerRegrDebug$selected_features()

Returns

character().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrDebug$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("mtcars")
learner = lrn("regr.debug", save_tasks = TRUE)
learner$train(task, row_ids = 1:20)
prediction = learner$predict(task, row_ids = 21:32)

learner$model$task_train
#> <TaskRegr:mtcars> (20 x 11): Motor Trends
#> * Target: mpg
#> * Properties: -
#> * Features (10):
#>   - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
learner$model$task_predict
#> <TaskRegr:mtcars> (12 x 11): Motor Trends
#> * Target: mpg
#> * Properties: -
#> * Features (10):
#>   - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt