# Prediction intervals

> Prediction intervals - Designed for DataRobot trial users, experience an end-to-end DataRobot
> workflow using a use case that predicts flight delays.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.572153+00:00` (UTC).

## Primary page

- [Prediction intervals](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/pred-intervals-inf.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Advanced analytics and tools](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/prediction_intervals_via_conformal_inference/prediction_intervals_via_conformal_inference.ipynb)

This AI Accelerator demonstrates various ways for generating prediction intervals for any DataRobot model. The methods presented here are rooted in the area of conformal inference (also known as conformal prediction). These types of approaches have become increasingly popular for uncertainty quantification because they do not require strict distributional assumptions to be met in order to achieve proper coverage (i.e., they only require that the testing data is exchangeable with the training data). While conformal inference can be applied across a wide array of prediction problems, the focus in this notebook will be prediction interval generation on regression targets.