Benchmarking uncertainty quantification for engineering regression under graded distribution shift
Read the original on arXiv Machine Learning →The paper introduces a graded distribution‑shift benchmark for evaluating uncertainty quantification (UQ) in engineering regression models. It defines a three‑rung shift ladder—distribution, soft, and hard shift—using physically meaningful variables and tests 12 UQ methods across seven public engineering datasets, including extrapolation, turbofan fault, and air‑quality scenarios. Results show conformal methods maintain high coverage under distribution shift, but coverage drops significantly under hard shift, with no clear method outperforming others, and weighted conformalised quantile regression often abstains from predictions.
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