# Projected random forests and conformal prediction of circular data

arXiv: https://arxiv.org/abs/2410.24145 · HTML: https://mlsanity.com/paper/2410.24145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Valid prediction intervals for regression problems](https://mlsanity.com/paper/2107.00363/) (2021)
- [Universal distribution of the empirical coverage in split conformal prediction](https://mlsanity.com/paper/2303.02770/) (2023)
- [Recent advances in directional statistics](https://mlsanity.com/paper/2005.06889/) (2020)
- [Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting](https://mlsanity.com/paper/2001.00412/) (2020)
- [Conformal prediction for frequency-severity modeling](https://mlsanity.com/paper/2307.13124/) (2023)

## Abstract (arXiv)

We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under the assumption of data exchangeability. Leveraging the high performance of existing predictive models designed for linear responses, we analyze a general projection procedure that converts any linear-response regression model into one suitable for circular responses. When random forests are used as base models in this projection procedure, we leverage the random forest out-of-bag mechanism to eliminate the need for a separate calibration sample in the construction of prediction sets. On synthetic and real datasets, the resulting projected random forest model produces more efficient out-of-bag conformal prediction sets, with shorter median arc length, than the split conformal prediction sets generated by two existing alternative models.