Undecidability of Underfitting in Learning Algorithms

Sonia Sehra; David Flores; George D. Montanez

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arXiv · 2102.02850

Undecidability of Underfitting in Learning Algorithms

Sonia Sehra·David Flores·George D. Montanez

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Abstract

Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, even if given unlimited training time, is undecidable. We discuss the importance of this result and potential topics for further research, including information-theoretic and probabilistic strategies for bounding learning algorithm fit.

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cs.LG cs.AI cs.FL cs.IT math.IT

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Sonia Sehra, David Flores, George D. Montanez. 2021-02-04. Undecidability of Underfitting in Learning Algorithms. https://arxiv.org/abs/2102.02850

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