building-conformal-prediction-set

What problem does it solve?

It turns uncertain model outputs into prediction sets or intervals with a documented finite-sample coverage guarantee, so you can report calibrated uncertainty instead of a bare point estimate or probability score.

Core Features & Use Cases

• Split-conformal workflow: Uses disjoint train, calibration, and test splits to compute a valid conformal quantile and verify empirical coverage.
• Classification and regression support: Handles probabilistic classifiers, absolute-residual regression intervals, and conformalized quantile regression for heteroscedastic targets.
• Practical guardrails: Flags exchangeability problems, requires reporting set size or interval width, and refuses to pretend a guarantee exists when the data split is not valid.

Quick Start

Ask the skill to build a split-conformal setup for your model, compute the calibration quantile, and report held-out coverage plus average set size or interval width.