Predict Confidence Intervals for Classification via LLA

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

For each output, this uses Monte Carlo sampling from the Laplace posterior over logits, transforms to probability scale via sigmoid / softmax, and returns per-class quantile-based intervals.

Usage

laplace_conf_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.