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

Participant-grouped generalization workflow

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

data <- simulate_gazepoint_governed_data(21L, 6L, 1L, seed = 2401L)
predictors <- c("tracking_ratio", "blink_rate", "gaze_dispersion")
task <- create_gazepoint_synthetic_task(data, "recording_quality", "new_participants")
manifest <- create_gazepoint_synthetic_manifest(task$outcome, predictors)
folds <- create_gazepoint_group_folds(
  data, task$outcome, predictors, manifest,
  "new_participants", "participant_id", "trial_id", "stimulus_id",
  v = 3L, repeats = 2L, seed = 2401L
)
audit_gazepoint_group_folds(folds)
evaluation <- evaluate_gazepoint_group_folds(
  folds, task, predictors, "glm", seed = 2401L
)
summary <- summarize_gazepoint_resample_performance(evaluation)
summary

Every participant is assigned as a group. Row-level assessment metrics describe predictions under this grouped design; they are not participant-level measurements.