Reproduce “Decomposing Observational Multiplicity in Decision Trees: Leaf and Structural Regret” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Mustafa Cavus
Researchers at Eskisehir Technical University developed a framework to decompose observational multiplicity in decision trees into "leaf regret" (within-leaf noise) and "structural regret" (tree instability), revealing that structural instability is the primary driver of predictive uncertainty. The study demonstrated that these measures can effectively improve model safety in high-stakes applications by enabling selective prediction.
Key insights
- Total predictive variability in decision trees can be accurately decomposed into distinct leaf (aleatoric) and structural (epistemic) components, even for non-smooth models.
- Structural regret is the dominant source of observational multiplicity in decision trees, indicating that the instability of tree partition boundaries contributes significantly more to predictive uncertainty than local label noise within leaves.
- Quantifying these distinct sources of regret provides a diagnostic tool for algorithmic safety, enabling models to identify and abstain from predictions in regions of high arbitrariness.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.