Evaluate Per-Fold Model Performance — borg_fold

Fits a model on each fold's training set and evaluates on its test set, returning per-fold metrics with spatial centroids for geographic performance mapping.

Usage

borg_fold_performance(
  data,
  folds,
  formula,
  coords = NULL,
  metric = c("rmse", "mae", "rsq", "auc", "tss", "kappa", "sensitivity", "specificity",
    "accuracy"),
  fit_fun = stats::lm,
  parallel = FALSE
)

Arguments

data: Data frame with predictor and target columns.
folds: A borg_cv object, or a list of fold lists (each with $train and $test index vectors).
formula: A model formula (e.g. y ~ x1 + x2).
coords: Character vector of length 2. Coordinate column names for computing fold centroids. Optional.
metric: Character. Performance metric: "rmse" (default), "mae", or "rsq".
fit_fun: Function to fit a model. Default: lm. Must accept formula and data arguments and return an object with a predict() method.
parallel: Logical. If TRUE and the future.apply package is installed, fold evaluation runs in parallel. Default: FALSE.

Value

A data frame with columns:

fold: Fold index
metric: Metric name
value: Metric value
n_train: Training set size
n_test: Test set size
centroid_x, centroid_y: Spatial centroid of test set (if coords provided)

Has class "borg_fold_perf" with an autoplot() method.

Examples

set.seed(42)
d <- data.frame(
  x = runif(200, 0, 100), y = runif(200, 0, 100),
  z = rnorm(200)
)
cv <- borg_cv(d, coords = c("x", "y"), target = "z")
perf <- borg_fold_performance(d, cv, z ~ x + y, coords = c("x", "y"))
perf