Sparse Probabilistic Splits for Tree Ensembles: A Tunable Regularizer for Stable Generalization on Tabular Data

This paper proposes a regularization technique called sparse probabilistic split (SPS), which introduces controlled randomness into the node-splitting process of decision trees to mitigate overfitting and enhance generalization. Building on this technique, we propose two models: the sparse probabili...

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Үндсэн зохиолчид:	Jongkwan Choi, Yujin Lee, Eunbin Yun, Gyeongtaek Lee
Формат:	Artigo
Хэл сонгох:	Inglês
Хэвлэсэн:	IEEE 2026-01-01
Цуврал:	IEEE Access
Нөхцлүүд:	Decision trees sparse probabilistic split ensemble learning model generalization random splitting
Онлайн хандалт:	https://ieeexplore.ieee.org/document/11339487/
Шошгууд:	Шошго нэмэх Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!