Mondrian Conformal Prediction Intervals (Group-Conditional)

Constructs prediction intervals with group-conditional coverage guarantees. Instead of a single conformal quantile, a separate quantile is computed for each group, ensuring coverage within each subgroup (e.g. by gender, region, or model type).

Usage

conformal_mondrian(
  x,
  y,
  model,
  x_new,
  groups,
  groups_new,
  alpha = 0.1,
  cal_fraction = 0.5,
  seed = NULL
)

Arguments

x

A numeric matrix or data frame of predictor variables.

y

A numeric vector of response values.

model

A fitted model object, a make_model() specification, or a formula.

x_new

A numeric matrix or data frame of new predictor variables.

groups

A factor or character vector of group labels for each observation in x, with length equal to nrow(x).

groups_new

A factor or character vector of group labels for each observation in x_new, with length equal to nrow(x_new).

alpha

Miscoverage level. Default 0.10.

cal_fraction

Fraction of data used for calibration. Default 0.5.

seed

Optional random seed.

Value

A predictset_reg object. See conformal_split() for details. The method component is "mondrian". Additional components include groups_new (the group labels for new data) and group_quantiles (named numeric vector of per-group conformal quantiles).

References

Vovk, V., Gammerman, A. and Shafer, G. (2005). Algorithmic Learning in a Random World. Springer.

See also

Other regression methods: conformal_aci(), conformal_cqr(), conformal_cv(), conformal_jackknife(), conformal_split(), conformal_weighted()

Examples

set.seed(42)
n <- 400
x <- matrix(rnorm(n * 3), ncol = 3)
groups <- factor(ifelse(x[, 1] > 0, "high", "low"))
y <- x[, 1] * 2 + ifelse(groups == "high", 2, 0.5) * rnorm(n)
x_new <- matrix(rnorm(50 * 3), ncol = 3)
groups_new <- factor(ifelse(x_new[, 1] > 0, "high", "low"))

# \donttest{
result <- conformal_mondrian(x, y, model = y ~ ., x_new = x_new,
                              groups = groups, groups_new = groups_new)
print(result)
#> 
#> ── Conformal Prediction Intervals (Mondrian (Group-Conditional)) ───────────────
#> • Coverage target: "90%"
#> • Training: 200 | Calibration: 200 | Predictions: 50
#> • Conformal quantile: 2.3299
#> • Median interval width: 6.0854
# }