Evaluate nested grouped resampling with inner governed tuning

Usage

evaluate_gazepoint_nested_resampling(
  nested_folds,
  task,
  tuning_grid,
  selection_metric,
  direction,
  predictors = NULL,
  minimum_success_prop = 0.8,
  tie_breakers = NULL,
  selection_rationale = .gp3ml_nested_selection_rationale_default,
  seed = 1L,
  keep_models = FALSE,
  continue_on_error = TRUE
)

# S3 method for class 'gp3ml_nested_evaluation'
print(x, ...)

Arguments

nested_folds: A gp3ml_nested_folds object.
task: Governed task.
tuning_grid: Explicit tuning grid.
selection_metric: Explicit inner selection metric.
direction: Explicit selection direction.
predictors: Optional predictors.
minimum_success_prop: Minimum inner-fold success proportion.
tie_breakers: Optional secondary metrics.
selection_rationale: Human rationale recorded for each outer fold.
seed: Base deterministic seed.
keep_models: Whether outer fitted models are retained.
continue_on_error: Whether failed outer folds remain in the result.
x: An object returned by the corresponding gp3ml constructor, evaluator, summarizer, or validator.
...: Additional arguments passed to the print method.

Value

A gp3ml_nested_evaluation object retaining inner tuning results, selections, outer predictions, metrics, and failures.