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 single-covariate regressions |
s |
the subset of interest |
cv_folds |
the CV folds |
sample_splitting |
logical; should we use sample-splitting for predictiveness estimation? |
ss_folds |
the sample-splitting folds; only used if
|
split |
the split to use for sample-splitting; 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 hold-out data for each v in 1 through V
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