← 研踪 PaperTrailarXiv:2602.04042 · v3
Partition Tree: Conditional Density Estimation over General Outcome Spaces
首次提交 2026-02-04 06:12
版本更新 2026-11-07 09:24
雷达观测 2026-11-10 11:14
摘要
We propose Partition Tree, a tree-based framework for conditional density estimation over general outcome spaces that supports both continuous and categorical variables within a unified formulation. Our approach models conditional distributions as piecewise-constant densities on data-adaptive partitions and learns trees by greedily minimizing a surrogate for conditional log-loss. This yields a scalable alternative to existing probabilistic trees that does not make parametric assumptions about the target distribution. We further introduce Partition Forest, an ensemble extension obtained by averaging conditional densities. Empirically, we demonstrate improved probabilistic prediction over CART-style trees and competitive performance compared to state-of-the-art probabilistic tree methods and Random Forests.
作者备注：Code available at https://github.com/felipeangelimvieira/partition_tree
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-07 09:24 · 最新观测版本
Partition Tree: Conditional Density Estimation over General Outcome Spaces
We propose Partition Tree, a tree-based framework for conditional density estimation over general outcome spaces that supports both continuous and categorical variables within a unified formulation. Our approach models conditional distributions as piecewise-constant densities on data-adaptive partitions and learns trees by greedily minimizing a surrogate for conditional log-loss. This yields a scalable alternative to existing probabilistic trees that does not make parametric assumptions about the target distribution. We further introduce Partition Forest, an ensemble extension obtained by averaging conditional densities. Empirically, we demonstrate improved probabilistic prediction over CART-style trees and competitive performance compared to state-of-the-art probabilistic tree methods and Random Forests.
查看此版本解读 →查看此版本原文 ↗