Predict Prediction Intervals for Classification via LLA — laplace_pred_int

Predict-time entry point for classification prediction intervals. Called by parsnip via c(pkg = "kerasnip", fun = "laplace_pred_int_cls").

After computing epistemic probability samples (as in conf_int), this draws class labels from Bernoulli(p_sample) or Categorical(p_sample) and returns per-class quantiles of the resulting indicator samples — matching the posterior predictive behaviour of Stan and BART classification engines.

Usage

laplace_pred_int_cls(object, x, laplace_data, lvl, level = 0.95)

Arguments

object: The raw Keras model (from object$fit$fit).
x: Processed predictor data (matrix or array).
laplace_data: A named list of Laplace posterior data (from object$fit$laplace).
lvl: Character vector of class level names (from object$fit$lvl).
level: Confidence level (default 0.95).

Value

For single-output: a data frame with per-class .pred_lower_Level and .pred_upper_Level columns.