Estimates the ensemble weights of a superlearner by minimizing
the cross-validated MSE as a convex combination of the candidate
predictions, i.e. by least squares regression of the response on the
candidate predictions subject to the constraint that the weights are
non-negative and sum to one.
Usage
metalearner_convexcomb(y, pred, ...)
Arguments
- y
(numeric) Response vector.
- pred
(matrix) Matrix of cross-validated predictions with one column
per candidate learner.
- ...
Additional arguments (currently ignored).
Value
(numeric) Vector of ensemble weights, one element per column of
pred.
