提出PRAXIS算法，高效计算决策树的近似最优解集。
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
- 用新算法近似计算稀疏决策树的近似最优解集
- 相比传统方法，内存和运行时间降低多个数量级
- 适合需要可解释性与模型多样性分析的研究者
标准机器学习流程常存在大量性能接近最优的模型，这些“拉什蒙德集”为不确定性感知和鲁棒决策带来挑战与机遇。它们允许用户融入领域知识和偏好，量化给定数据集与目标函数下有效模型的多样性。然而，即使对简单的可解释模型类如稀疏决策树，计算拉什蒙德集仍需巨大内存和运行时资源。我们提出PRAXIS算法，显著提升计算效率，实现多个数量级的运行时间和内存优化。验证表明，PRAXIS通常能恢复几乎全部完整拉什蒙德集。该方法使研究者和从业者可在真实数据集上可扩展地建模拉什蒙德集。PRAXIS代码已公开于 https://github.com/zakk-h/PRAXIS。
原文摘要 · Abstract (English)
Standard machine learning pipelines often admit many near-optimal models. These "Rashomon sets" pose a range of challenges and opportunities for uncertainty-aware, robust decision making. They allow users to incorporate domain knowledge and preferences that would otherwise be difficult to specify directly in an objective, and they quantify diversity among valid models for a given training dataset and objective function. However, computation of Rashomon sets, even for simple, interpretable model classes such as sparse decision trees, continues to require immense memory and runtime resources. We present PRAXIS, an algorithm to approximate this Rashomon set with orders of magnitude improvement in runtime and memory usage. We validate that PRAXIS regularly recovers almost all of the full Rashomon set. PRAXIS allows researchers and practitioners to scalably model the Rashomon set for real-world datasets. Code for PRAXIS is available at https://github.com/zakk-h/PRAXIS
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