aiqbench analysis
This research introduces a projection procedure that adapts linear-response models, specifically random forests, to handle circular data regression while maintaining finite-sample coverage guarantees. By leveraging the out-of-bag mechanism, the method eliminates the need for a separate calibration sample, resulting in more efficient prediction sets with shorter median arc lengths compared to existing alternatives. The approach offers a practical solution for domains involving directional data, such as wind direction or time-of-day analysis, without requiring new model architectures.
Projected random forests and conformal prediction of circular data
arXiv:2410.24145v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2410.24145 [stat.ML]
(or arXiv:2410.24145v3 [stat.ML] for this version)
From: Paulo C. Marques F. [view email] [v1] Thu, 31 Oct 2024 17:05:52 UTC (202 KB) [v2] Thu, 26 Dec 2024 04:11:19 UTC (202 KB) [v3] Tue, 9 Jun 2026 19:53:34 UTC (330 KB)
Full text under the publisher's open licence · 182 words
Related coverage
An Uncertainty-Aware Hybrid Mathematical-Machine-Learning Model for Smart Irrigation Decision Support
arXiv · Sep 15, 2026
Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees
arXiv · Jul 21, 2026
Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes
arXiv · Sep 17, 2026
Beyond Point Prediction: Artificial Representative Trees with Uncertainty
arXiv · Sep 22, 2026