Learner that encapsulates the Super Learner algorithm. Fits metalearner on cross-validated predictions from learners. Then forms a pipeline with the learners.


R6Class object.


Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.



The "library" of learners to include


The metalearner to be fit on predictions from the library.

If null, default_metalearner is used to construct a metalearner based on the outcome_type of the training task.

An origami folds object. If NULL, folds from the task are used.


Stores all sub-parts of the SL computation. When set to FALSE the resultant object has a memory footprint that is significantly reduced through the discarding of intermediary data structures.


Not used.

Common Parameters

Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared by all learners.


A character vector of covariates. The learner will use this to subset the covariates for any specified task


A variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified


All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating