Random search hyperparameter optimisation (R parity) — morie_random_search

Uses caret::train with search = "random".

Usage

morie_random_search_cv(
  x,
  y,
  method = NULL,
  n_iter = 20L,
  cv = 5L,
  task = "auto",
  seed = 0L,
  deterministic_seed = NULL
)

x: Numeric predictor matrix.
y: Response.
method: caret method id (default by task).
n_iter: Number of random draws.
cv: CV folds.
task: "auto" / "classification" / "regression".
seed: RNG seed.
deterministic_seed: Integer or NULL. If supplied, the RNG state is derived from the SHA-keyed morie_det_rng() so Py<->R streams agree on the canonical fixture. When NULL (default), behaviour is unchanged: seed drives set.seed() directly.

Named list: estimate, best_params, best_score, sampled_params, sampled_scores, n_iter, task, n, method.

# See the package vignettes for usage examples:
#   vignette(package = "rmorie")