Beyond Point Prediction: Artificial Representative Trees with Uncertainty
Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models for RFs, but their use as standalone prediction models with uncertainty quantification has not been systematically investigated. We combine ARTs with leaf-wise Mondrian conformal predictive systems (CPS), enabling a single tree to provide continuous predictions, prediction intervals, and probabilities of exceeding arbitrary thresholds. We compared ARTs with CPS against decision trees with CPS and separate regression and probability trees across five simulation scenarios, 21 benchmark datasets...
| Authors | Lea L. Mairhöfer, Silke Szymczak, Björn-Hergen Laabs et al. |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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