Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.
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References
Cooper, A. F., Moss, E., Laufer, B. & Nissenbaum, H. In 2022 ACM Conf. Fairness, Accountability, and Transparency 864–876 (2022).
Molnar, C., Casalicchio, G. & Bischl, B. In Joint European Conf. Machine Learning and Knowledge Discovery in Databases 417–431 (2020).
Minh, D., Wang, H. X., Li, Y. F. & Nguyen, T. N. Artif. Intell. Rev. 55, 3503–3568 (2021).
Arrieta, A. B. et al. Inform. Fusion 58, 82–115 (2020).
Sculley, D., Snoek, J., Wiltschko, A. B. & Rahimi, A. In 6th Int. Conf. Learning Representations (ICLR, 2018).
Vapnik, V. The Nature of Statistical Learning Theory (Springer, 2000).
Lindsey, J. K. Applying Generalized Linear Models (Springer Science & Business Media, 2000).
Raissi, M., Perdikaris, P. & Karniadakis, G. E. J. Comput. Phys. 378, 686–707 (2019).
Wu, Z. et al. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2020).
Acknowledgements
D.M. was funded by grants 22/06211-2 and 23/00256-7, São Paulo Research Foundation (FAPESP). J.B. was funded by grants 14/50937-1 and 2020/06950-4, São Paulo Research Foundation (FAPESP).
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Marcondes, D., Simonis, A. & Barrera, J. Back to basics to open the black box. Nat Mach Intell 6, 498–501 (2024). https://doi.org/10.1038/s42256-024-00842-6
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DOI: https://doi.org/10.1038/s42256-024-00842-6