研究人员通过利用Bregman散度（一类泛化了欧氏距离的损失函数族）引入了一个决策树的新型框架。该方法提供了一种统一的方法，可将决策树适配到各种统计模型和几何结构中，超越了CART等现有算法中常用的特设不纯度标准。该研究还深入探讨了这些广义树的理论特性，考察了生成凸函数的特性如何影响其稳定性和一致性。 AI
影响 这项研究为决策树算法提供了一个统一的理论基础，有望在各种统计应用中实现更具适应性和鲁棒性的模型。
排序理由 该集群包含一篇详细介绍决策树新理论框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2407.02458v3 Announce Type: replace-cross Abstract: This work studies the statistical implications of using features comprised of general linear combinations of covariates to partition the data in randomized decision tree and forest regression algorithms. Using random tesse…
arXiv:2606.13984v1 Announce Type: new Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures. Among them, the Classification and Regression Trees, introduced …
Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures. Among them, the Classification and Regression Trees, introduced by Breiman, Friedman, Olshen, and Stone in 1984,…