All Models Are Wrong, but a Set of them is Useful

Oregon Convention Center 

Prediction performance in practical settings often relies on a single, complex model, which may sacrifice interpretability. The concept of the Rashomon set challenges this paradigm by advocating for a set of equally-performing models rather than a singular one. We introduce the Sparse Wrapper Algorithm (SWAG), a novel multi-model selection method that employs a greedy algorithm combining screening and wrapper approaches. SWAG produces a set of low-dimensional models with high predictive power, offering practitioners the flexibility to choose models aligned with their needs or domain expertise without compromising accuracy. SWAG works in a forward step manner; the user selects a learning mechanism and the SWAG begins by evaluating low-dimensional models. It then systematically builds larger models based on the best-performing ones from previous steps. It results with a set of models called "SWAG models." SWAG's modeling flexibility empowers decision-makers in diverse fields such as genomics, engineering, and neurology. Its adaptability allows it to construct a network revealing the intensity and direction of attribute interactions, providing a more insightful perspective.

Prediction accuracy

multi-model selection

SWAG

Interpretability

Feature importance 

International Statistical Institute