Learner that encapsulates the Super Learner algorithm. Fits metalearner on cross-validated predictions from learners. Then forms a pipeline with the learners.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
learners: The "library" of user-specified algorithms for the
super learner to consider as candidates.
metalearner = "default": The metalearner to be fit on c
cross-validated predictions from the candidates. If "default",
the default_metalearner is used to construct a
metalearner based on the outcome_type of the training
task.
cv_control = NULL: Optional list of arguments that will be used
to define a specific cross-validation fold structure for fitting the
super learner. Intended for use in a nested cross-validation scheme,
such as cross-validated super learner (cv_sl) or
when Lrnr_sl is considered in the list of candidate
learners in another Lrnr_sl. Includes the arguments
listed below, and any others to be passed to
fold_funs:
strata = NULL: Discrete covariate or outcome name to
define stratified cross-validation folds. If NULL and if
task$outcome_type$type is binary or categorical, then the
default behavior is to consider stratified cross-validation, where
the strata are defined with respect to the outcome. To override
the default behavior, i.e., to not consider stratified
cross-validation when strata = NULL and
task$outcome_type$type is binary or categorical is not
NULL, set strata = "none".
cluster_by_id = TRUE: Logical to specify clustered
cross-validation scheme according to id in task.
Specifically, if task$nodes$id is not NULL and if
cluster_by_id = TRUE (default) then task$nodes$id
is used to define a clustered cross-validation scheme, so
dependent units are placed together in the same training sets
and validation set. To override the default behavior, i.e., to not
consider clustered cross-validation when task$nodes$id is
not NULL, set cluster_by_id = FALSE.
fold_fun = NULL: A function indicating the origami
cross-validation scheme to use, such as
folds_vfold for V-fold cross-validation.
See fold_funs for a list of possibilities.
If NULL (default) and if other cv_control arguments
are specified, e.g., V, strata or
cluster_by_id, then the default behavior is to set
fold_fun = origami::folds_vfold.
...: Other arguments to be passed to fold_fun, such as
V for fold_fun = folds_vfold. See
fold_funs for a list fold-function-specific
possible arguments.
keep_extra = TRUE: Stores all sub-parts of the super learner
computation. When FALSE, the resulting object has a memory
footprint that is significantly reduced through the discarding of
intermediary data structures.
verbose = NULL: Whether to print cv_control-related
messages. Warnings and errors are always printed. When
verbose = NULL, verbosity specified by option
sl3.verbose will be used, and the default sl3.verbose
option is FALSE. (Note: to turn on sl3.verbose option,
set options("sl3.verbose" = TRUE).)
...: Any additional parameters that can be considered by
Lrnr_base.
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_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
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
if (FALSE) {
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
# this is just for illustrative purposes, not intended for real applications
# of the super learner!
glm_lrn <- Lrnr_glm$new()
ranger_lrn <- Lrnr_ranger$new()
lasso_lrn <- Lrnr_glmnet$new()
eSL <- Lrnr_sl$new(learners = list(glm_lrn, ranger_lrn, lasso_lrn))
eSL_fit <- eSL$train(task)
# example with cv_control, where Lrnr_sl included as a candidate
eSL_nested5folds <- Lrnr_sl$new(
learners = list(glm_lrn, ranger_lrn, lasso_lrn),
cv_control = list(V = 5),
verbose = FALSE
)
dSL <- Lrnr_sl$new(
learners = list(glm_lrn, ranger_lrn, lasso_lrn, eSL_nested5folds),
metalearner = Lrnr_cv_selector$new(loss_squared_error)
)
dSL_fit <- dSL$train(task)
# example with cv_control, where we use cross-validated super learner
cvSL_fit <- cv_sl(
lrnr_sl = eSL_nested5folds, task = task, eval_fun = loss_squared_error
)
}