### Prediction Intervals in Tidymodels

Lighting Talk, 1:25-1:30

In the evolving landscape of statistical modeling and machine learning, the tidymodels framework has emerged as a powerful suite of packages that streamline the predictive modeling process in R and that fit nicely within the greater tidyverse. While predictions get more attention, in many contexts you are asked not just to produce a point estimate but also a range of potential values for each individual prediction. In this talk, I will provide a very brief overview of the tidymodels ecosystem followed by a discussion of the different methods you may want to use to produce prediction intervals and how these may be outputted using tidymodels. Primarily I will focus on regression contexts (i.e. when your target of interest is continuous) and will touch on analytic methods, quantile based approaches, as well as simulation / conformal inference based approaches. I wrote a series of posts on these topics a couple of years ago that I will draw from in crafting the talk: * Understanding Prediction Intervals: Why youâ€™d want prediction intervals, sources of uncertainty and how to output prediction intervals analytically like for Linear Regression https://www.bryanshalloway.com/2021/03/18/intuition-on-uncertainty-of-predictions-introduction-to-prediction-intervals/ * Quantile Regression Forests for Prediction Intervals: quantile methods (e.g. in the context of Random Forests) for producing prediction intervals: https://www.bryanshalloway.com/2021/04/21/quantile-regression-forests-for-prediction-intervals/ * Simulating Prediction Intervals: a broadly generalizable way of producing prediction intervals by simulation. https://www.bryanshalloway.com/2021/04/05/simulating-prediction-intervals/ I will summarize and update the content from these posts (e.g. the code in them is not up-to-date with the current tidymodels API) and focus more on conformal inference. In this latter aim, I will draw heavily from materials produced by Max Kuhn, e.g. his Posit Conf 2023 talk describing support for conformal inference now available in the {probably} package (https://www.youtube.com/watch?v=vJ4BYJSg734 ). I would also provide some intuition on how to think about conformal inference based prediction intervals, synthesizing tidymodelsâ€™ documentation with materials from Anastasios N. Angelopoulos and Stephen Bates (e.g. from this presentation and the associated paper: https://www.youtube.com/watch?v=nql000Lu_iE ). Although there are some reasonably niche/advanced topics here I would keep the talk as high-level and intuitive as possible.

## Pronouns: he/him## Seattle, WABryan lives in Seattle. He has worked in Data Science at NetApp since 2017 where he has led projects on a wide range of problems with different teams in customer support, sales, and pricing. |