Fit the combiner on the out-of-fold predictions

The combiner sees the out-of-fold predictions, the response, the mask of scorable cells and the fold map, and never a model, so it cannot read anything a candidate fitted in-sample.

Usage

ensemble_fit(oof, y, cells, folds, spec = ensemble(), scores = NULL)

Arguments

oof

Named list of [target, response] matrices, one per candidate.

y

The response matrix.

cells

The scorable-cell mask from scorable_cells().

folds

The fold map.

spec

An ensemble() specification.

scores

The per-cell scores of the run, a data frame carrying candidate, variable, fold, score and scorable. Read only where spec names no metric of its own.

Value

A timesift_stack: method, the named weights, and what they were fitted on.

Details

The weights are fitted to the response on those predictions, so the combined prediction scored against the same response is scored on the data its weights were fitted to, and that score is optimistic. timesift() evaluates the stack the other way: each outer fold's weights are fitted on inner out-of-fold predictions of its training targets and applied to the outer test fold.

Examples

set.seed(1)
y <- matrix(rbinom(200, 1, 0.4), nrow = 50,
            dimnames = list(sprintf("p%02d", 1:50), paste0("sp", 1:4)))
folds <- fold_map(y, v = 5)
truth <- matrix(runif(200), nrow = 50, dimnames = dimnames(y))
oof <- list(good = 0.8 * y + 0.2 * truth, noise = truth)
ensemble_fit(oof, y, scorable_cells(y, folds), folds)