Mondrian Conformal Prediction Sets for Classification

Constructs prediction sets with group-conditional coverage guarantees for classification. Uses LAC-style scoring with per-group conformal quantiles.

Usage

conformal_mondrian_class(
  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 factor (or character/integer vector coerced to factor) of class labels.

model

A make_model() specification with type = "classification", or a fitted model object.

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.

groups_new

A factor or character vector of group labels for each observation in 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_class object. See conformal_lac() for details. The method component is "mondrian". Additional components include groups_new and group_quantiles.

See also

Other classification methods: conformal_aps(), conformal_lac(), conformal_raps()

Examples

set.seed(42)
n <- 400
x <- matrix(rnorm(n * 4), ncol = 4)
groups <- factor(ifelse(x[, 1] > 0, "high", "low"))
y <- factor(ifelse(x[,1] + x[,2] > 0, "A", "B"))
x_new <- matrix(rnorm(50 * 4), ncol = 4)
groups_new <- factor(ifelse(x_new[, 1] > 0, "high", "low"))

clf <- make_model(
  train_fun = function(x, y) glm(y ~ ., data = data.frame(y = y, x),
                                  family = "binomial"),
  predict_fun = function(object, x_new) {
    df <- as.data.frame(x_new)
    names(df) <- paste0("X", seq_len(ncol(x_new)))
    p <- predict(object, newdata = df, type = "response")
    cbind(A = 1 - p, B = p)
  },
  type = "classification"
)

# \donttest{
result <- conformal_mondrian_class(x, y, model = clf, x_new = x_new,
                                    groups = groups, groups_new = groups_new)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
print(result)
#> 
#> ── Conformal Prediction Sets (Mondrian (Group-Conditional)) ────────────────────
#> • Coverage target: "90%"
#> • Classes: "A, B"
#> • Training: 200 | Calibration: 200 | Predictions: 50
#> • Median set size: 1 | Mean set size: 1
# }