Description
For many problems concerning prediction, providing intervals is more useful than just offering point estimates. This talk will provide an overview of:
- How to think about uncertainty in your predictions (e.g., noise in the data vs uncertainty in estimation)
- Approaches to producing prediction intervals (e.g., parametric vs conformal)
- Measures and considerations when evaluating and training models for prediction intervals
While I will touch on some similar topics as Max Kuhn’s posit::conf(2023) talk on conformal inference, my talk will cover different points and have a broader focus. I hope attendees gain an understanding of some of the key tools and concepts related to prediction intervals and that they leave inspired to learn more.