# Prediction intervals
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/pred-intervals-inf.html

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

# Prediction intervals

[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.

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