论文标题
使用基于模型的树和增强的树木适合低阶功能方差分析模型
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models
论文作者
论文摘要
在机器学习（ML）社区中，低阶功能方差分析模型以固有的可解释的机器学习为幌子重新发现。可解释的提升机或EBM（Lou等人，2013年）和Gami-Net（Yang等，2021）是最近提出的两种用于拟合功能性主要效应和二阶相互作用的ML算法。我们提出了一种称为Gami-Tree的新算法，它类似于EBM，但具有许多可以提高性能的功能。它使用基于模型的树作为基础学习者，并结合了一种新的交互过滤方法，该方法更好地捕获了基础交互。此外，我们的迭代训练方法会收敛到具有更好的预测性能的模型，并且嵌入式纯化可确保相互作用在层次上是正交的。该算法不需要广泛的调整，我们的实施速度快速有效。我们使用模拟和真实数据集比较Gami-Tree与EBM和GAMI-NET的性能和解释性。
Low-order functional ANOVA (fANOVA) models have been rediscovered in the machine learning (ML) community under the guise of inherently interpretable machine learning. Explainable Boosting Machines or EBM (Lou et al. 2013) and GAMI-Net (Yang et al. 2021) are two recently proposed ML algorithms for fitting functional main effects and second-order interactions. We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance. It uses model-based trees as base learners and incorporates a new interaction filtering method that is better at capturing the underlying interactions. In addition, our iterative training method converges to a model with better predictive performance, and the embedded purification ensures that interactions are hierarchically orthogonal to main effects. The algorithm does not need extensive tuning, and our implementation is fast and efficient. We use simulated and real datasets to compare the performance and interpretability of GAMI-Tree with EBM and GAMI-Net.
