laplace_pred_int_reg: Predict Prediction Intervals via Last-Layer Laplace...

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

Predict Prediction Intervals via Last-Layer Laplace Approximation

Description

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

For each output in the model, this builds the per-sample predictive variance (uncertainty on a new observation Y|X = epistemic variance + observation noise) from the stored Laplace posterior and returns symmetric Normal-based intervals at the requested level.

Usage

laplace_pred_int_reg(object, x, laplace_data, 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, one entry per output (from object$fit$laplace). Each entry contains h_diag, tau, sigma_sq_noise, n_training, and combined_model.
level	Confidence level (default 0.95). Passed through from predict(..., type = "conf_int", level = 0.95).

Value

A named list of matrices (one per output), each with columns .pred, .pred_lower, and .pred_upper.

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