Evaluate the fitness of an individual

evaluate_fitness(
  ind,
  data,
  target_col,
  task = "classification",
  cv_folds = 3,
  evaluation_strategy = "cv",
  split_ids = NULL,
  shared_splits = NULL,
  evaluator = "lightgbm",
  fold_ids = NULL,
  shared_folds = NULL,
  shared_full = NULL,
  state_cache = NULL,
  threads = 8
)

Arguments

ind: An evo_individual object.
data: A data.frame or data.table containing the dataset.
target_col: Name of the target column.
task: "classification" or "regression".
cv_folds: Number of cross-validation folds.
evaluation_strategy: Character string, either "cv" (cross-validation) or "split" (train/validation split).
split_ids: Optional vector of pre-defined split assignments (e.g. "train", "val", "holdout").
shared_splits: Optional list of shared data.table splits for in-place caching.
evaluator: The ML model to use ("lightgbm" or "xgboost").
fold_ids: Optional vector of pre-defined fold assignments.
shared_folds: Optional list of shared data.table CV folds for in-place caching.
shared_full: Optional data.table of the full dataset for in-place caching.
state_cache: Optional environment to cache full-dataset fitted states of stateful transformers.
threads: Number of threads to use for parallel execution (default 8)