Aggregate Models, Not Explanations: Improving Feature Importance Estimation
This addresses the need for reliable feature-importance estimation in critical biomedical domains, offering an incremental improvement over existing ensembling approaches.
The paper tackles the problem of unstable feature-importance estimates in machine learning models, which undermines their use in biomedical applications, and shows that ensembling at the model level rather than aggregating explanations reduces error and improves accuracy, validated on benchmarks and a proteomic study.
Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. However, due to data sampling and algorithmic stochasticity, expressive models can be unstable, leading to inaccurate variable importance estimates and undermining their utility in critical biomedical applications. Although ensembling offers a solution, deciding whether to explain a single ensemble model or aggregate individual model explanations is difficult due to the nonlinearity of importance measures and remains largely understudied. Our theoretical analysis, developed under assumptions accommodating complex state-of-the-art ML models, reveals that this choice is primarily driven by the model's excess risk. In contrast to prior literature, we show that ensembling at the model level provides more accurate variable-importance estimates, particularly for expressive models, by reducing this leading error term. We validate these findings on classical benchmarks and a large-scale proteomic study from the UK Biobank.