Hugging Face Trending Papers August 20, 2026

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

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The paper introduces DICS, a clustering-based framework that uses data-informed priors to construct a compact set of candidate splits for decision tree classifiers. By incorporating class-aware structure, DICS reduces the split search space, preserving predictive performance while cutting training time. The authors provide theoretical analysis and experimental results showing comparable accuracy to exhaustive search across synthetic and benchmark datasets.

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