论文标题
树流：超越基于树的高斯概率回归
TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
论文作者
论文摘要
基于树的合奏以其在分类和回归问题方面的出色表现而闻名，其特征是特征向量，这些特征向量由各种范围和域的混合型变量表示。但是，考虑回归问题，它们主要旨在提供确定性响应或用高斯或参数分布建模输出的不确定性。在这项工作中，我们介绍了TreeFlow，这是基于树的方法，该方法结合了使用Tree Emembles和使用标准化流量的灵活概率分布进行建模的功能。解决方案的主要思想是将基于树的模型用作特征提取器，并将其与标准化流量的条件变体结合使用。因此，我们的方法能够为回归输出建模复杂分布。我们评估了提出的方法，这些方法具有不同的量，特征特征和目标维度的挑战回归基准。与基于树的回归基线相比，我们在具有多模式目标分布的数据集上获得了具有多模式目标分布的概率和确定性指标的SOTA结果。
The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we introduce TreeFlow, the tree-based approach that combines the benefits of using tree ensembles with the capabilities of modeling flexible probability distributions using normalizing flows. The main idea of the solution is to use a tree-based model as a feature extractor and combine it with a conditional variant of normalizing flow. Consequently, our approach is capable of modeling complex distributions for the regression outputs. We evaluate the proposed method on challenging regression benchmarks with varying volume, feature characteristics, and target dimensionality. We obtain the SOTA results for both probabilistic and deterministic metrics on datasets with multi-modal target distributions and competitive results on unimodal ones compared to tree-based regression baselines.
