Governed Decision Thresholds and Abstention
In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

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

This article separates probability estimation from the scientific decision rule. gp3ml does not treat 0.5 as a universally justified decision threshold. Thresholds must be predeclared or selected using analysis/inner-resampling data, never an outer assessment or independent external-validation set.

truth <- factor(rep(c("pass", "review"), 20), levels = c("pass", "review"))
probability <- seq(0.05, 0.95, length.out = 40)

evaluation <- evaluate_gazepoint_thresholds(
  truth = truth,
  probability = probability,
  positive = "review",
  thresholds = seq(0.2, 0.8, by = 0.05)
)

rule <- select_gazepoint_threshold(
  evaluation,
  metric = "balanced_accuracy",
  direction = "maximize",
  generalization_target = "new_participants",
  scientific_justification =
    "Balance sensitivity and specificity for predefined recording-quality review status."
)

validate_gazepoint_decision_rule(rule, require_threshold = TRUE)
plot(evaluation)

An abstention interval can be declared when the scientific protocol permits withholding a forced classification. Abstentions must be reported explicitly, including coverage and error among non-abstained predictions.