mlr3 (version 0.13.1)

mlr_learners_regr.debug: Regression Learner for Debugging

Description

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”

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

• Required Packages: mlr3

Parameters

Id	Type	Default	Range	Levels
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	-	\([0, 1]\)	-

Super classes

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

Methods

Public methods

• LearnerRegrDebug$new()

• LearnerRegrDebug$clone()

• 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()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrDebug$new()

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.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#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.featureless, mlr_learners_classif.rpart, mlr_learners_regr.featureless, mlr_learners_regr.rpart, mlr_learners

Examples

# NOT RUN {
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
learner$model$task_predict
# }