Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective

• URL: http://arxiv.org/abs/2211.11680v1
• Date: Mon, 21 Nov 2022 17:48:44 GMT
• Title: Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
• Authors: Alice E. A. Allen, Alexandre Tkatchenko
• Abstract summary: We show how flexible non-linear solutions will not always improve upon manually adding transforms and interactions between variables to linear regression models. We discuss how to recognize this before constructing a data-driven model and how such analysis can help us move to intrinsically interpretable regression models.
• Score: 77.53142165205281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can machine learning enable similar leaps in the natural and social sciences? This is certainly the expectation in many scientific fields and recent years have seen a plethora of applications of non-linear models to a wide range of datasets. However, flexible non-linear solutions will not always improve upon manually adding transforms and interactions between variables to linear regression models. We discuss how to recognize this before constructing a data-driven model and how such analysis can help us move to intrinsically interpretable regression models. Furthermore, for a variety of applications in the natural and social sciences we demonstrate why improvements may be seen with more complex regression models and why they may not.

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