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Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective by Allen, Alice E. A.; Tkatchenko, Alexandre is a scholarly article available to read on EtoBox.

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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.

Author

Allen, Alice E. A.; Tkatchenko, Alexandre

Published

2022

Language

EN