This meta-learner provides fitting procedures for any pairing of loss
function and metalearner function, subject to constraints. The
optimization problem is solved by making use of
optim, For further
details, consult the documentation of
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
A function(alpha, X) that takes a vector of covariates and a matrix of data and combines them into a vector of predictions. See metalearners for options.
A function(pred, truth) that takes prediction and truth vectors and returns a loss vector. See loss_functions for options.
If true, X includes an intercept term.
If true, alpha is initialized to all 0's, useful for TMLE. Otherwise, it is initialized to equal weights summing to 1, useful for Super Learner.
Not currently 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.
A character vector of covariates. The learner will use this to subset the covariates for any specified task
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
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