{
  "abstract": "Classification and regression trees are typically constructed using a greedy splitting rule that maximizes the immediate reduction in prediction error at each node. Although this strategy is computationally efficient, it can miss splits that yield small short-term gains but create substantial downstream improvements after further partitioning. We propose a look-ahead tree-building method that evaluates each candidate split by the prediction error reduction achieved after growing a conventional CART subtree below that split. Because the full look-ahead procedure can be computationally expensive, we also describe a smart look-ahead algorithm that learns downstream split values using node-level features. The proposed framework preserves the interpretability of recursive partitioning while improving split selection in hierarchical or interaction-driven settings. We conduct a simulation study comparing conventional, full look-ahead, and smart look-ahead methods under several settings and apply the proposed methods to analyze two real data examples demonstrating the merit of the new methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16440",
  "author_total": 4,
  "authors": [
    {
      "name": "Andrew Gao"
    },
    {
      "name": "Tianlin Liu"
    },
    {
      "name": "Ruichen Han"
    },
    {
      "name": "Lu Tian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/68ewcz4xkyrr6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16440",
  "fulltext_url": null,
  "id": "68ewcz4xkyrr6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16440",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1710|Data Mining Algorithms and Applications"
    ]
  ],
  "openalex": "W7213398616",
  "pdf_url": "https://arxiv.org/pdf/2609.16440",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/68ewcz4xkyrr6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "2609.16440",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16440"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Learned Look-Ahead Splitting Rule for CART",
  "title_text": "Learned Look-Ahead Splitting Rule for CART",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/68ewcz4xkyrr6",
  "venue": "arXiv",
  "versions": []
}