Predefined recording-quality review status
In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(gp3ml.reproducible_examples = TRUE)
library(gp3ml)

Scope

This workflow predicts a predefined recording-quality review status. It does not infer health, emotion, cognition, intent, identity, or any latent state. Predictions support manual quality review.

data <- simulate_gazepoint_governed_data(18L, 6L, 1L, seed = 2101L)
predictors <- c("tracking_ratio", "blink_rate", "gaze_dispersion")
task <- create_gazepoint_synthetic_task(
  data, "recording_quality", "new_participants"
)
manifest <- create_gazepoint_synthetic_manifest(task$outcome, predictors)

Materialized grouped folds

folds <- create_gazepoint_group_folds(
  data = data,
  outcome = task$outcome,
  predictors = predictors,
  feature_manifest = manifest,
  generalization_target = task$generalization_target,
  participant_id = task$participant_id,
  trial_id = task$unit_id,
  stimulus_id = task$stimulus_id,
  v = 3L,
  repeats = 2L,
  seed = 2101L
)
folds$validation

Fold-local evaluation

evaluation <- evaluate_gazepoint_group_folds(
  folds,
  task,
  predictors = predictors,
  engine = "glm",
  seed = 2101L,
  assess_calibration = TRUE,
  calibration_bootstrap = 0L
)
evaluation
summarize_gazepoint_resample_performance(evaluation)

Interpretation

The metrics describe assessment-row predictions generated under participant-grouped resampling. They must not be relabelled as participant-level outcomes.