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Create a fairness evaluation data object

Source: R/fairness-data.R
fairness_data.Rd

Bundles predictions, labels, and protected attributes into a standardized container for fairness analysis.

Usage

fairness_data(
  predictions,
  labels,
  protected_attr,
  threshold = 0.5,
  reference_group = NULL
)

Arguments

predictions

Numeric vector of predicted probabilities or risk scores (between 0 and 1).

labels

Binary integer vector of true outcomes (0 or 1).

protected_attr

Character or factor vector identifying the protected group membership (e.g., race, sex, age group).

threshold

Decision threshold for converting probabilities to binary predictions. Default 0.5.

reference_group

Name of the reference (privileged) group. If NULL, the group with the highest selection rate is used.

Value

A fairness_data object (list) with standardized components: predictions, labels, protected, threshold, predicted_class, reference_group, groups, n, prevalence.

Examples

set.seed(42)
fd <- fairness_data(
  predictions = runif(200),
  labels = rbinom(200, 1, 0.3),
  protected_attr = sample(c("GroupA", "GroupB"), 200, replace = TRUE)
)
fd
#> 
#> ── Fairness evaluation data 
#> n = 200 | prevalence = 0.265
#> Groups: "GroupA, GroupB"
#> Reference: "GroupB" | Threshold: 0.5