Summary of Projected Random Forests and Conformal Prediction Of Circular Data, by Paulo C. Marques F. et al.
Projected random forests and conformal prediction of circular data
by Paulo C. Marques F., Rinaldo Artes, Helton Graziadei
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to regression problems with circular responses is proposed in this paper. By introducing a suitable conformity score, the authors apply split conformal prediction techniques to generate prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under exchangeable data. Building on existing models designed for linear responses, a general projection procedure is analyzed that converts these models into ones suitable for circular responses. The use of random forests as basis models enables the elimination of the need for a separate calibration sample in the construction of prediction sets. The resulting projected random forests model produces more efficient out-of-bag conformal prediction sets compared to existing alternative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes regression problems with circular responses and makes them easier to solve. It uses special techniques called split conformal prediction to make predictions that are accurate and cover a certain area. The authors also use an existing method for linear responses and adapt it to work with circular responses. This helps create better predictions without needing extra data. The results show that the new approach works well on synthetic and real datasets. |
Keywords
» Artificial intelligence » Regression
