Exploring the cloud of feature interaction scores in a Rashomon set

TL;DR

This paper introduces the feature interaction score (FIS) within a Rashomon set to analyze the variability of feature interactions across multiple equally accurate models, providing deeper insights into model behavior.

Contribution

It proposes a novel FIS method for exploring feature interactions across a set of similar-performing models, along with visualization tools like Halo and swarm plots.

Findings

FIS reveals significant variation in feature interactions among models.

Visualizations help interpret interaction importance in high-dimensional models.

Experiments demonstrate the method's effectiveness on real-world datasets.

Abstract

Interactions among features are central to understanding the behavior of machine learning models. Recent research has made significant strides in detecting and quantifying feature interactions in single predictive models. However, we argue that the feature interactions extracted from a single pre-specified model may not be trustworthy since: a well-trained predictive model may not preserve the true feature interactions and there exist multiple well-performing predictive models that differ in feature interaction strengths. Thus, we recommend exploring feature interaction strengths in a model class of approximately equally accurate predictive models. In this work, we introduce the feature interaction score (FIS) in the context of a Rashomon set, representing a collection of models that achieve similar accuracy on a given task. We propose a general and practical algorithm to calculate the FIS in the model class. We demonstrate the properties of the FIS via synthetic data and draw connections to other areas of statistics. Additionally, we introduce a Halo plot for visualizing the feature interaction variance in high-dimensional space and a swarm plot for analyzing FIS in a Rashomon set. Experiments with recidivism prediction and image classification illustrate how feature interactions can vary dramatically in importance for similarly accurate predictive models. Our results suggest that the proposed FIS can provide valuable insights into the nature of feature interactions in machine learning models.