run_sl  R Documentation 
Run a Super Learner for the provided subset of features
run_sl( Y = NULL, X = NULL, V = 5, SL.library = "SL.glm", univariate_SL.library = NULL, s = 1, cv_folds = NULL, sample_splitting = TRUE, ss_folds = NULL, split = 1, verbose = FALSE, progress_bar = NULL, indx = 1, weights = rep(1, nrow(X)), cross_fitted_se = TRUE, full = NULL, vector = TRUE, ... )
Y 
the outcome 
X 
the covariates 
V 
the number of folds 
SL.library 
the library of candidate learners 
univariate_SL.library 
the library of candidate learners for singlecovariate regressions 
s 
the subset of interest 
cv_folds 
the CV folds 
sample_splitting 
logical; should we use samplesplitting for predictiveness estimation? 
ss_folds 
the samplesplitting folds; only used if

split 
the split to use for samplesplitting; only used if

verbose 
should we print progress? defaults to FALSE 
progress_bar 
the progress bar to print to (only if verbose = TRUE) 
indx 
the index to pass to progress bar (only if verbose = TRUE) 
weights 
weights to pass to estimation procedure 
cross_fitted_se 
if 
full 
should this be considered a "full" or "reduced" regression?
If 
vector 
should we return a vector ( 
... 
other arguments to Super Learner 
a list of length V, with the results of predicting on the holdout data for each v in 1 through V
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