Takes a lists of Task, a list of Learner and a list of Resampling to
generate a design in an expand.grid() fashion (a.k.a. cross join or Cartesian product).
Resampling strategies may not be instantiated, and will be instantiated per task internally.
expand_grid(tasks, learners, resamplings):: list of Task.
:: list of Learner.
:: list of Resampling.
(data.table::data.table()) with the cross product of the input vectors.
The mlr3 package provides some shortcuts to ease the creation of its objects.
First, instead of an object, it is possible to pass a string identifier which is used to lookup the object in a mlr3misc::Dictionary:
Learner in mlr_learners.
Measure in mlr_measures.
Additionally, each task type has an associated default measure (stored in mlr_reflections) which is used as a fallback if no other measure is provided. Classification tasks default to the classification error in "classif.ce", regression tasks to the mean squared error in "regr.mse".