scientific-uncertainty-quantification

You can compute calibrated prediction intervals using conformal prediction, MC dropout, and deep ensemble methods to generate reliable probabilistic estimates and prediction sets for both regression and classification tasks.

Conformal prediction is a distribution-free technique used to generate mathematically guaranteed prediction intervals and sets. You need it when applying calibrated probabilistic estimates to increase trust in your machine learning model outputs.

You can separate epistemic and aleatoric uncertainty by applying MC dropout and deep ensemble methods to your trained models, allowing you to quantify and distinguish data noise from model uncertainty across diverse datasets.

Yes, you can perform calibration analysis and Expected Calibration Error (ECE) estimation to assess probability calibration across your models, ensuring your predictive uncertainty outputs are reliable for downstream decision-making.

The best way is to use integrated benchmarking utilities and ToolUniverse integration to run reproducible uncertainty quantification scenarios, evaluating conformal prediction, MC dropout, and deep ensembles across diverse datasets.

You need a trained machine learning model to quantify its predictive uncertainty. You can run the quick start scenario on your trained model to immediately generate calibrated intervals, prediction sets, and calibration metrics.