Forward Feature Selection with Spatial/Blocked CV

Selects variables using blocked cross-validation instead of random CV, avoiding overfitting to spatial/temporal structure. At each step, adds the variable that most improves the CV metric.

Usage

borg_forward_selection(
  data,
  target,
  predictors = NULL,
  folds = NULL,
  coords = NULL,
  time = NULL,
  groups = NULL,
  metric = c("rmse", "mae", "rsq"),
  fit_fun = stats::lm,
  min_vars = 1L,
  verbose = FALSE
)

Arguments

data

Data frame.

target

Character. Response variable name.

predictors

Character vector. Candidate predictor names. If NULL, uses all numeric columns except target and coords.

folds

A borg_cv object or fold list. If NULL, generated automatically via borg_cv().

coords

Character vector of coordinate columns (for auto CV).

time

Character. Time column (for auto CV).

groups

Character. Group column (for auto CV).

metric

Character. "rmse" (default), "mae", or "rsq".

fit_fun

Function. Model fitting function. Default: lm.

min_vars

Integer. Minimum variables to include. Default: 1.

verbose

Logical. Default: FALSE.

Value

A list with class "borg_ffs" containing:

selected

Character vector of selected variable names (in order)

history

Data frame: step, variable_added, metric_value, n_vars

best_metric

Best CV metric achieved

all_vars

All candidate variables

Details

Similar to CAST::ffs() but uses BORG's CV infrastructure and supports any dependency structure (spatial, temporal, grouped).

Examples

set.seed(42)
d <- data.frame(
  x = runif(100), y = runif(100),
  important = rnorm(100), noise1 = rnorm(100), noise2 = rnorm(100)
)
d$z <- d$important * 2 + rnorm(100, sd = 0.5)
ffs <- borg_forward_selection(d, target = "z",
                                predictors = c("important", "noise1", "noise2"),
                                coords = c("x", "y"))
ffs$selected