gazepoint_classification_metrics: Binary classification metrics

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

Binary classification metrics

Description

Binary classification metrics

Usage

gazepoint_classification_metrics(
  truth,
  probability,
  predicted = NULL,
  positive = NULL,
  threshold = 0.5
)

Arguments

truth	Observed binary outcome values.
probability	Predicted positive-class probabilities.
predicted	Optional predicted classes.
positive	Label representing the positive class.
threshold	Probability threshold used for class predictions.

Value

A one-row data frame containing the sample size, threshold, class-performance measures, discrimination metrics, Brier score, and log loss.

Examples

truth <- factor(
  rep(c("pass", "review"), 6),
  levels = c("pass", "review")
)
probability <- c(
  0.20, 0.70, 0.60, 0.55, 0.30, 0.80,
  0.65, 0.45, 0.40, 0.75, 0.50, 0.60
)
predicted <- factor(
  ifelse(probability >= 0.5, "review", "pass"),
  levels = levels(truth)
)
gazepoint_classification_metrics(
  truth = truth,
  probability = probability,
  predicted = predicted,
  positive = "review"
)

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