Pre-registration for Predictive Modeling

• URL: http://arxiv.org/abs/2311.18807v1
• Date: Thu, 30 Nov 2023 18:52:10 GMT
• Title: Pre-registration for Predictive Modeling
• Authors: Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma, Duncan J. Watts, Jessica Hullman
• Abstract summary: We explore the possibility and potential benefits of introducing pre-registration to the field of predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.
• Score: 26.112782996222617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field. Despite notable advancements in predictive modeling, spanning core machine learning tasks to various scientific applications, challenges such as overlooked contextual factors, data-dependent decision-making, and unintentional re-use of test data have raised questions about the integrity of results. To address these issues, we propose adapting pre-registration practices from explanatory modeling to predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers to gain insight into the effectiveness of pre-registration in preventing biased estimates and promoting more reliable research outcomes. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.

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