Discrete meta learner

Implements the discrete super learner: the candidate learner with the lowest risk (computed via the model.score argument of superlearner) is given weight one and all other learners weight zero.

Usage

metalearner_discrete(y, pred, model.score, ...)

Arguments

y

(numeric) Response vector.

pred

(matrix) Matrix of cross-validated predictions with one column per candidate learner.

model.score

(function) Method for scoring the predictions of each base learner.

...

Additional arguments (currently ignored).

Value

(numeric) Vector of ensemble weights, one element per column of pred.

See also

superlearner learner_sl