laplace_conf_int_cls: Predict Confidence Intervals for Classification via LLA

In kerasnip: A Bridge Between 'keras' and 'tidymodels'

Predict Confidence Intervals for Classification via LLA

Description

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.

kerasnip documentation built on Sept. 4, 2026, 1:06 a.m.