Ensemble Predictions from CV Fold Models — borg

Combines predictions from models fitted on each CV fold into a weighted ensemble. Each fold's model predicts on the full dataset (or new data), and predictions are averaged with optional performance-based weighting.

Usage

borg_ensemble(
  data,
  folds,
  formula,
  newdata = NULL,
  fit_fun = stats::lm,
  weight_by = c("equal", "performance"),
  metric = c("rmse", "mae")
)

Arguments

data: Data frame used for training.
folds: A borg_cv object or fold list.
formula: Model formula.
newdata: Optional data frame for prediction. If NULL, predicts on data.
fit_fun: Function. Default: lm.
weight_by: Character. Weighting scheme: "equal" (default) or "performance" (inverse error weight).
metric: Character. Metric for performance weighting. Default: "rmse".

Value

A list with class "borg_ensemble" containing:

prediction: Numeric vector of ensemble predictions
uncertainty: Per-observation SD across fold predictions
weights: Fold weights used
n_models: Number of contributing models

Examples

set.seed(42)
d <- data.frame(x = runif(100), y = runif(100), z = rnorm(100))
cv <- borg_cv(d, coords = c("x", "y"), target = "z")
ens <- borg_ensemble(d, cv, z ~ x + y)
cor(d$z, ens$prediction)