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.

`learners`

The "library" of learners to include

`metalearner`

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.
`folds=NULL`

An

`origami`

folds object. If`NULL`

, folds from the task are used.`keep_extra=TRUE`

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.

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

, and shared
by all learners.

`covariates`

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

`outcome_type`

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

Other Learners:
`Custom_chain`

,
`Lrnr_HarmonicReg`

,
`Lrnr_arima`

,
`Lrnr_bartMachine`

,
`Lrnr_base`

,
`Lrnr_bayesglm`

,
`Lrnr_bilstm`

,
`Lrnr_caret`

,
`Lrnr_cv_selector`

,
`Lrnr_cv`

,
`Lrnr_dbarts`

,
`Lrnr_define_interactions`

,
`Lrnr_density_discretize`

,
`Lrnr_density_hse`

,
`Lrnr_density_semiparametric`

,
`Lrnr_earth`

,
`Lrnr_expSmooth`

,
`Lrnr_gam`

,
`Lrnr_ga`

,
`Lrnr_gbm`

,
`Lrnr_glm_fast`

,
`Lrnr_glmnet`

,
`Lrnr_glm`

,
`Lrnr_grf`

,
`Lrnr_gru_keras`

,
`Lrnr_gts`

,
`Lrnr_h2o_grid`

,
`Lrnr_hal9001`

,
`Lrnr_haldensify`

,
`Lrnr_hts`

,
`Lrnr_independent_binomial`

,
`Lrnr_lightgbm`

,
`Lrnr_lstm_keras`

,
`Lrnr_mean`

,
`Lrnr_multiple_ts`

,
`Lrnr_multivariate`

,
`Lrnr_nnet`

,
`Lrnr_nnls`

,
`Lrnr_optim`

,
`Lrnr_pca`

,
`Lrnr_pkg_SuperLearner`

,
`Lrnr_polspline`

,
`Lrnr_pooled_hazards`

,
`Lrnr_randomForest`

,
`Lrnr_ranger`

,
`Lrnr_revere_task`

,
`Lrnr_rpart`

,
`Lrnr_rugarch`

,
`Lrnr_screener_augment`

,
`Lrnr_screener_coefs`

,
`Lrnr_screener_correlation`

,
`Lrnr_screener_importance`

,
`Lrnr_solnp_density`

,
`Lrnr_solnp`

,
`Lrnr_stratified`

,
`Lrnr_subset_covariates`

,
`Lrnr_svm`

,
`Lrnr_tsDyn`

,
`Lrnr_ts_weights`

,
`Lrnr_xgboost`

,
`Pipeline`

,
`Stack`

,
`define_h2o_X()`

,
`undocumented_learner`