evaluate_gazepoint_group_folds: Evaluate a governed model specification across materialized...

In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

Evaluate a governed model specification across materialized grouped folds

Description

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 documentation built on Aug. 23, 2026, 5:11 p.m.