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Ternary decision trees add uncertainty zones to improve accuracy

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced ternary decision trees, which enhance standard binary decision trees by incorporating an uncertainty zone around decision boundaries. This zone allows for weighted blending of predictions from child subtrees and flags uncertain instances for different downstream handling. Five novel methods for estimating the uncertainty zone's width were proposed and evaluated, demonstrating significant improvements in accuracy over traditional CART methods across numerous datasets. AI

IMPACT Introduces a novel method for decision trees that improves accuracy by explicitly modeling uncertainty at decision boundaries.

RANK_REASON The cluster contains two academic papers detailing novel methods for decision tree algorithms.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Ternary decision trees add uncertainty zones to improve accuracy

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · William Smits ·

    Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

    arXiv:2605.22740v1 Announce Type: new Abstract: Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to those directly on it. We introduce ternary decision trees, which augment each s…

  2. arXiv cs.LG TIER_1 English(EN) · William Smits ·

    Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

    Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to those directly on it. We introduce ternary decision trees, which augment each split node with an uncertainty zone of half-width…

  3. arXiv stat.ML TIER_1 English(EN) · Martin Bladt, Rasmus Frigaard Lemvig ·

    Consistency of Honest Decision Trees and Random Forests

    arXiv:2601.14991v2 Announce Type: replace-cross Abstract: We study various types of consistency of honest decision trees and random forests in the regression setting. In contrast to related literature, our proofs are elementary and follow the classical arguments used for smoothin…

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