How the candidates are combined — ensemble • timesift

Every candidate emits an out-of-fold prediction for every scorable cell over the same folds, so the combination is arithmetic on those predictions and nothing else. ensemble() says which arithmetic.

Usage

ensemble(
  method = c("stack", "mean", "median", "weighted"),
  scope = c("all", "learners", "representations"),
  metric = NULL,
  response = NULL
)

Arguments

method: How the members are combined.
scope: Which candidates are eligible.
metric: Name of the registered metric the eligibility and the "weighted" weights are read by, or NULL for the score the run already carries.
response: Name of the registered response head whose loss "stack" minimises, or NULL for the head the run was fitted under. Naming a head the run does not fit toward is an error rather than an override, and ensemble_fit() called on its own reads NULL as "presence_absence".

Value

A timesift_ensemble.

Details

"stack" fits non-negative weights summing to one on the out-of-fold predictions alone, never on in-sample ones, minimising the response head's loss over the scorable cells: binomial deviance for presence-absence. One weight vector covers every response, because per-response weights would be fitted on the handful of cells a rare response has. "mean" and "median" combine without fitting anything. "weighted" takes each candidate's own mean score, keeps its non-negative part and rescales those to sum to one, so a candidate scoring at or below zero is left out and the rest are weighted by how well they scored.

scope says which candidates are eligible. "all" is every candidate. "learners" keeps the several learners that read the representation of the best-scoring candidate, and "representations" keeps the one learner of the best-scoring candidate across the representations it ran on; both are read off the same mean scores the report shows.

Examples

ensemble()
ensemble("weighted", scope = "learners")