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

IdTypeDefaultRangeLevels
predict_missingnumeric0\([0, 1]\)-
predict_missing_typecharacterna-na, omit
save_taskslogicalFALSE-TRUE, FALSE
threadsinteger-\([1, \infty)\)-
xnumeric-\([0, 1]\)-

See also

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

Super classes

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

Methods

Public methods

Inherited methods

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.

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)
#> * 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)
#> * Target: mpg
#> * Properties: -
#> * Features (10):
#>   - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt