Learned Look-Ahead Splitting Rule for CART
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-le...
| Authors | Andrew Gao, Tianlin Liu, Ruichen Han et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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