Lrnr_sl: SuperLearner Algorithm

Description Usage Format Value Parameters Common Parameters See Also


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.


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

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bilstm, Lrnr_condensier, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_expSmooth, Lrnr_glm_fast, Lrnr_glmnet, Lrnr_glm, Lrnr_grf, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_independent_binomial, Lrnr_lstm, Lrnr_mean, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_randomForest, Lrnr_ranger, Lrnr_rpart, Lrnr_rugarch, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_xgboost, Pipeline, Stack, define_h2o_X, undocumented_learner

jeremyrcoyle/sl3 documentation built on Dec. 6, 2018, 7:15 p.m.