Evaluate a governed model specification across materialized grouped folds

Fits preprocessing and the requested model only on each fold's analysis partition, predicts only on the corresponding assessment partition, retains excluded rows, and records fold-level metrics, leakage audits, warnings, and failures. Row-level predictions are never relabelled as participant- or stimulus-level estimates.

Usage

evaluate_gazepoint_group_folds(
  folds,
  task,
  predictors = NULL,
  engine = NULL,
  preprocessor_args = list(),
  engine_args = list(),
  threshold = 0.5,
  seed = 1L,
  assess_calibration = FALSE,
  calibration_bins = 10L,
  calibration_bootstrap = 0L,
  keep_models = FALSE,
  continue_on_error = TRUE
)

# S3 method for class 'gp3ml_resample_evaluation'
print(x, ...)

Arguments

folds: A mature gazepoint_group_folds object containing materialized folds under folds$folds.
task: A governed gp3ml_task compatible with the fold metadata.
predictors: Optional predictor names. Defaults to the fold metadata.
engine: Model engine name or governed custom engine.
preprocessor_args: Arguments passed to fit_gazepoint_preprocessor().
engine_args: Arguments passed to fit_gazepoint_model().
threshold: Classification threshold.
seed: Base deterministic seed.
assess_calibration: Whether to calculate assessment-fold calibration summaries for classification tasks.
calibration_bins: Number of reliability bins.
calibration_bootstrap: Calibration bootstrap replicates. Use zero in fast smoke tests.
keep_models: Whether fitted fold models are retained.
continue_on_error: Whether later folds continue after a failed fold.
x: An object returned by the corresponding gp3ml constructor, evaluator, summarizer, or validator.
...: Additional arguments passed to the print method.

Value

A gp3ml_resample_evaluation object.

Examples

data <- simulate_gazepoint_governed_data(12L, 4L, 1L, 101L)
predictors <- c("tracking_ratio", "blink_rate", "gaze_dispersion")
manifest <- create_gazepoint_synthetic_manifest("quality_status", predictors)
folds <- create_gazepoint_group_folds(
  data = data,
  outcome = "quality_status",
  predictors = predictors,
  feature_manifest = manifest,
  generalization_target = "new_participants",
  participant_id = "participant_id",
  trial_id = "trial_id",
  stimulus_id = "stimulus_id",
  v = 3L,
  repeats = 1L,
  seed = 101L
)
task <- create_gazepoint_synthetic_task(
  data,
  "recording_quality",
  "new_participants"
)
evaluation <- evaluate_gazepoint_group_folds(
  folds,
  task,
  predictors = predictors,
  engine = "glm",
  seed = 101L
)
evaluation
#> <gp3ml_resample_evaluation>
#>   Target: new_participants
#>   Engine: glm
#>   Folds: 3
#>   Passed/review/failed: 0/3/0
#>   Predictions: 48