PINE通过置信校准实现树模型压缩，兼顾精度与推理一致性。
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
- 基于置信校准控制可保留预测一致性的数据区域大小
- 在12个公开数据集上压缩率最高提升30%且保持精度稳定
- 适合对模型推理一致性有要求的场景如金融风控
树集成模型在表格数据上具备优异的预测性能和可解释性，仍被广泛使用。标准剪枝方法通常在准确率与压缩率间权衡，可能改变部分预测结果，影响决策一致性。忠实剪枝方法虽能保证整个输入空间的预测等价性，但压缩率较低。本文提出PINE，一种在分布内区域提供强保障的剪枝方法。PINE在该区域内保持预测等价性，并通过单个参数α利用置信校准控制区域大小。在12个公开表格数据集上的实验表明，PINE在保持与现有忠实剪枝方法相当预测性能的同时，压缩率最高提升30%。
原文摘要 · Abstract (English)
Tree ensembles are machine learning models with strong predictive performance and interpretability, and remain widely used for tabular data. Standard pruning methods for tree ensembles typically optimize an accuracy-compression trade-off and may change a subset of predictions, potentially compromising decision consistency. Faithful pruning methods address this issue by preserving prediction equivalence over the entire input space, but this requirement leads to lower compression ratios. We propose PINE, a pruning method that provides strong guarantees within an in-distribution region. PINE preserves prediction equivalence within this region and controls the region size using a single parameter $α$ via conformal calibration. Experiments on 12 public tabular datasets show that PINE improves the compression ratio by up to 30% while preserving predictions at a comparable level to existing faithful pruning methods.
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