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

Abstract Number:

3644 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yagmur Yavuzozdemir (1), roberto molinari (2), Gaetan Bakalli (3), Stéphane Guerrier (4), Cesare Miglioli (4), Samuel Orso (1), Nabil Mili (4)

Institutions:

(1) N/A, N/A, (2) Auburn University, N/A, (3) Emlyon Business School, N/A, (4) University of Geneva, N/A

Co-Author(s):

roberto molinari  
Auburn University
Gaetan Bakalli  
Emlyon Business School
Stéphane Guerrier  
University of Geneva
Cesare Miglioli  
University of Geneva
Samuel Orso  
N/A
Nabil Mili  
University of Geneva

First Author:

Yagmur Yavuzozdemir  
N/A

Presenting Author:

Yagmur Yavuzozdemir  
N/A

Abstract Text:

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.

Keywords:

Prediction accuracy|multi-model selection|SWAG|Interpretability|Feature importance|

Sponsors:

International Statistical Institute

Tracks:

Miscellaneous

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