TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
This work addresses the problem of modeling complex, non-Gaussian distributions in regression for machine learning practitioners, representing an incremental improvement over existing tree-based methods.
The authors tackled the limitation of tree-based ensembles in modeling only Gaussian or parametric distributions for regression outputs by introducing TreeFlow, which combines tree ensembles with normalizing flows to model flexible probability distributions, achieving state-of-the-art results on datasets with multi-modal target distributions and competitive results on unimodal ones.
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.