Monotone Tree-Based GAMI Models by Adapting XGBoost

TL;DR

This paper introduces monotone GAMI-Tree models that adapt XGBoost to incorporate monotonicity constraints, enabling interpretable and monotone functional interaction modeling.

Contribution

It develops a novel approach to fit monotone GAMI models by combining interaction filtering, monotone XGBoost fitting, and model parsing, enhancing interpretability and monotonicity.

Findings

Demonstrates the effectiveness of monotone GAMI-Tree on simulated data.

Shows the approach can identify important interactions.

Compares monotone GAMI-Tree with existing models like EBM.

Abstract

Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) models are directly interpretable as the functional main effects and interactions can be easily plotted and visualized. Unfortunately, it is not easy to incorporate the monotonicity requirement into the existing GAMI models based on boosted trees, such as EBM (Lou et al. 2013) and GAMI-Lin-T (Hu et al. 2022). This paper considers models of the form $f(x)=\sum_{j,k}f_{j,k}(x_j, x_k)$ and develops monotone tree-based GAMI models, called monotone GAMI-Tree, by adapting the XGBoost algorithm. It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost. However, the fitted model is still a black box. We take a different approach: i) use a filtering technique to determine the important interactions, ii) fit a monotone XGBoost algorithm with the selected interactions, and finally iii) parse and purify the results to get a monotone GAMI model. Simulated datasets are used to demonstrate the behaviors of mono-GAMI-Tree and EBM, both of which use piecewise constant fits. Note that the monotonicity requirement is for the full model. Under certain situations, the main effects will also be monotone. But, as seen in the examples, the interactions will not be monotone.