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.
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.
arXiv:2608. 08078v1 Announce Type: new Abstract: Interval prediction aims to achieve a target coverage level while producing intervals that are as short as possible.
arXiv:2605. 22740v2 Announce Type: replace Abstract: Decision trees assign identical confidence to instances near and far from each split threshold.