Target-Aware Conformal Prediction

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

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

Performance uncertainty and prediction uncertainty answer different questions. This workflow calibrates split-conformal prediction to an explicit unit. Grouped calibration uses the maximum row conformity score within each supplied unit, which is conservative and records the calibration semantics.

It does not assert distribution-free guarantees under arbitrary dependence.

truth <- c(1.0, 1.4, 2.0, 2.5, 3.0, 3.6)
prediction <- c(1.1, 1.3, 2.2, 2.4, 2.9, 3.4)
participant <- c("P1","P1","P2","P2","P3","P3")

fit <- fit_gazepoint_conformal(
  truth = truth,
  prediction = prediction,
  task_type = "regression",
  level = 0.90,
  calibration_unit = "participant",
  unit = participant,
  generalization_target = "new_participants"
)

interval <- predict_gazepoint_interval(fit, prediction)
coverage <- assess_gazepoint_conformal_coverage(
  fit, truth = truth, interval = interval, unit = participant
)

coverage
plot(coverage)

Do not describe observation-level coverage as new-participant coverage merely because participant identifiers are present elsewhere in the study.

gp3ml documentation built on Aug. 23, 2026, 5:11 p.m.