Beyond Point Prediction: Artificial Representative Trees with Uncertainty
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arXiv:2609.24528v1 Announce Type: cross Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs...
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.
The paper introduces the conditional expected local radius (CELR), a new tool for measuring local conditional probability concentration in general metric spaces. Using CELR, the authors develop a conformal prediction method that works for non‑Euclidean responses, accommodates heterogeneity and multimodality without requiring densities or vector‑space structure, and avoids costly transport comparisons. The method is implemented via a metric distributional random forest that handles multivariate covariates and nonlinear interactions, yielding uniform consistency, asymptotic conditional validity, and finite‑sample marginal coverage under exchangeability, while also providing substantial computational savings compared to existing approaches.
arXiv:2602. 22432v2 Announce Type: replace-cross Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty.
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
We introduce the conditional expected local radius (CELR) and develop a new conformal prediction method for non-Euclidean responses in general metric spaces. CELR measures local conditional probabilit...